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Screening tools for employment in clinical healthcare delivery systems: a content analysis

BMC Health Services Research volume 24, Article number: 720 (2024) Cite this article

Abstract

Background

The relationship between work and health is complex and bidirectional, where work can have both health-harming and health-enhancing effects. Though employment is recognized as a social determinant of health, and clinical healthcare delivery systems are increasingly using screening tools to ask patients about social needs, little research has explored the extent to which employment-related social risk is captured in these screening tools. This study aimed to identify and characterize employment- and work-related questions in social risk screening tools that have been implemented in clinical healthcare delivery systems.

Methods

We conducted a qualitative content analysis of employment-related items in screening tools that have been implemented in clinical healthcare service delivery systems. Three content areas guided data extraction and analysis: Setting, Domain, and Level of Contextualization.

Results

Screening tools that asked employment-related questions were implemented in settings that were diverse in the populations served and the scope of care provided. The intent of employment-related items focused on four domains: Social Risk Factor, Social Need, Employment Exposure, and Legal Need. Most questions were found to have a low Level of Contextualization and were largely focused on identifying an individual’s employment status.

Conclusions

Several existing screening tools include measures of employment-related social risk, but these items do not have a clear purpose and range widely depending on the setting in which they are implemented. In order to maximize the utility of these tools, clinical healthcare delivery systems should carefully consider what domain(s) they aim to capture and how they anticipate using the screening tools to address social determinants of health.

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Background

Work is an important determinant of health inequities [1]. Work opportunities, and the corresponding risks and benefits, are strongly shaped by race, ethnicity, gender, age, class, and geography. Work influences where one fits in social and economic hierarchies, and is linked to education, income, and power [2,3,4].

The relationship between work and health is multidimensional and complex [2]. Work and health influence one another, and work can have both health-harming and health-enhancing effects [2, 3]. Work is a source of income and, in the United States, a key determinant of health insurance and access to healthcare [3]. As such, unemployment—a traditional measure of work, and precarious employment—relating to concepts like employment strain and employment uncertainty [5, 6], are important risk factors and have been linked to various adverse health outcomes [7, 8]. Work also determines exposure to environmental and occupational hazards, and is a source of psychological strain for many workers [9]. The complex ways work influences health have led to calls for using occupation or occupational prestige as an indicator of socioeconomic status beyond income and education [10].

Clinical healthcare delivery systems increasingly recognize the importance of identifying and addressing social determinants of health [9, 11,12,13,14], and conversations about screening for social risk factors have moved into the mainstream [14,15,16]. Screening patients within clinical healthcare delivery settings can provide data for practical applications on an individual level, like adapting medical care; a system level, like developing new healthcare models; and a societal level, like implementing population health interventions [17, 18]. Evidence gathered from screening tools creates opportunities to acutely improve patients’ social circumstances by providing a direct pathway to connect individuals to non-clinical services and provides pathways for long-term improvements [19]. The coordination of these services also supports integrated care models and generates insight into strategies to reduce healthcare spending [20].

According to the 2022 “State of the Science” report summarizing the state of social screening in healthcare, the estimated prevalence of social risk screening ranges from 56 to 77% [16]. However, a recent systematic review found that there were a limited number of studies that reported the impact of social risk screening and interventions on clinical outcomes like process measures, short-term social needs outcomes, intermediate impact on health outcomes, and long-term/health care cost or utilization outcomes [21]. Additionally, the measures and outcomes used in the studies are highly variable and underscore the difficulty in comparing and synthesizing the available data.

Despite the variability, available evidence, like that from Yan et al., supports the positive impact of integrating SDoH or social needs screening into electronic health records (EHRs) on the process, healthcare cost, and utilization measures and suggest positive associations with short-, intermediate-, and long-term health outcomes [21]. To further investigate the impact of screening and related interventions, such as referrals to non-clinical services to address social risk, it is integral to generate evidence that can directly test for the association between screening for social risk factors in clinical healthcare delivery settings and clinical health outcomes. In particular, there is a need to better understand how social risk screening impacts specific patient populations; how characteristics of single domains of social risk, like employment, can be leveraged to tailor screening tools for more effective patient care; and how the implementation of related interventions can influence health outcomes [14].

Employment is widely considered a social determinant of health [22], but is frequently included exclusively as a subset of economic stability [23]. For example, the State of the Science report combines employment and income as a single screening domain [16], and a systematic review of employment interventions in healthcare settings focused exclusively on interventions to help patients gain employment [24]. Neither of these summaries describes a focus on the characteristics of work or working conditions as a social risk factor. Thus, findings from this study will provide a comprehensive overview of the current landscape (setting, domain, and level of contextualization) of employment as a social risk screening item in clinical healthcare delivery settings. Evidence of setting will provide insight into the groups of patients being screened for employment; evidence of domain will generate a more nuanced understanding of the intent of screening for employment; and evidence of screening item contextualization will depict the variation in the depth with which social risk is screened for. Given the robust evidence linking employment status, specific working conditions, and occupational exposures to adverse health outcomes, this study sought to identify and characterize employment- and work-related questions in screening tools that have been evaluated in clinical healthcare delivery systems.

Methods

We conducted a qualitative content analysis of employment-related items in screening tools that have been evaluated in clinical healthcare service delivery systems. An item is defined as the context of the question and its corresponding response options. A complete list of items (question and response options) can be found in Supplementary Table 1.

Data sources

The content analysis was based on literature from a related systematic review [25]. Using a comprehensive search strategy, we searched databases including MEDLINE, PsycINFO, SocINDEX, EMBASE, and the SIREN (Social Interventions and Research & Evaluation Network) Evidence and Resource library for studies that described screening tools for employment-related social risk factors published through February 14, 2022, as depicted in Fig. 1. In consultation with a reference librarian, we selected multiple search terms related to three domains: [1] employment or working conditions; [2] screening; and [3] healthcare settings. We supplemented our database search with hand searches of reference lists of included studies and by entering included studies into Scopus to identify citing articles. We also searched bibliographies of published systematic reviews that came up through our database search but excluded due to their study design. Our complete search strategy with all search terms is included in Supplementary File 1.

Fig. 1
figure 1

Source Page et al., 2021 [26]

PRISMA flowchart.

Full size image

Studies were selected for the content analysis if [1] screening practices and/or interventions were integrated into a clinical healthcare service delivery system and [2] the screening tool assessed individuals for some employment exposure (e.g., employment, work, work arrangements, working conditions). Articles were excluded if they were published in languages other than English, were not empirical, were identified as a duplicate, screened patient populations outside of healthcare settings (e.g., in workplaces or other community settings), or used a screening tool that did not assess any dimension of employment.

Rayyan systematic review software was used to screen the article title and abstract. Two reviewers (J.G., M.C.) independently screened the articles. Discrepancies were solved by open discussion between reviewers. Articles selected for full-text review were then obtained and imported into EndNote, where they were independently assessed by the same two reviewers for inclusion. The final content analysis included 44 articles, describing 30 unique screening tools that included at least one employment-related item.

Data extraction

Three content areas guided data extraction and analysis: Setting, Domain, and Level of Contextualization.

Setting

The setting was extracted from the article(s) describing each tool, and describes the clinical setting in which the screening tool was implemented. More than one setting listed next to a tool indicates that the tool was described in the context of multiple settings across the literature. Settings were diverse and included public health clinics, federally qualified health centers, hospitals, primary care practices, pediatric clinics, school-based clinics, community health centers, urgent care clinics, obstetric care facilities, and legal and homeless health clinics.

Domain

We characterized the primary intent of the question as the “domain.” We based our assessment on the objective(s) of the article in which the screening tool was described, the stated intent of the screening tool (if available), the screening tool sub-section in which the question was placed, and/or the language of the question itself. We identified four domains: Social Risk Factor, Social Need, Employment Exposure, and Legal Need. Questions in the Social Risk Factor domain sought to identify employment as an individual-level adverse social determinant of health—specifically, whether an individual lacked employment [27]. Those in the Social Need domain went beyond identifying employment status and included content that emphasized the patient’s role in prioritizing their needs and/or where its purpose within the screening tool was intended to provide a social intervention. The Employment Exposure domain captured questions that focus on occupational and environmental work exposures and/or working conditions, for example, prolonged standing or heavy lifting. Lastly, Legal Need asked about employment-related legal considerations.

Level of Contextualization

We characterized the Level of Contextualization based on the depth of inquiry into employment-related concerns and, correspondingly, the extent to which the screening tool could illuminate the context of the individual’s employment. We rated each set of items (per tool) Level 1–3, with higher Levels reflecting questions that capture more information about an individual’s employment context. Level 1 meant that the question asked whether an individual was employed or identified employment as a social need. Level 2 inquired about an individual’s type of employment or details about their work arrangement. This rating included responses that allowed individuals to identify the kind of work they engaged in (e.g., self-employed, homemaker) and details about their work arrangement (e.g., full-time, part-time, or temporary work). Level 3 was given for highly detailed content and in-depth questions about employment characteristics. These employment questions asked about specific workplace or occupational exposures and explored problems or barriers related to an individual’s particular work context.

Results

We identified 30 unique screening tools that contained employment-related items (Table 1).

Table 1 Characteristics of screening tools with employment-related items
Full size table

Setting

Screening tools with employment-related questions have been deployed in various healthcare service delivery systems (Table 1). Settings were diverse in the populations served and the scope of care provided. While many questions were asked in primary care settings, three notable populations of focus were identified in our content analysis as frequently appearing: pregnant, pediatric, and Veteran populations. The scope of care across settings also varied widely, and there was an observed spectrum of provision and subspecialty, with a range of examples including tertiary hospitals, school-based clinics, and community health centers.

Domain

Questions about employment vary substantially and highlight the numerous ways employment is conceptualized in screening tools. The most common Domain that employment-related content addressed was Social Need (n = 15), with half of the screening tools assessing employment related to an individual’s prioritized needs or intending to provide a social intervention (Table 1). Seven screening tools included content assessing employment as a Social Risk Factor and six as an Employment Exposure. Two tools included content to assess employment as a Legal Need.

Level of contextualization

Less than half of the items captured context beyond whether an individual is employed or needs employment. Most (n = 17) of the content assessed was rated a Level 1, primarily focusing on identifying an individual’s employment status. Item response options included binary (yes/no an individual is employed) and checkbox selections that asked participants to identify if employment was a need. Six of the items were rated a Level 2, with content related to the type of employment or work arrangements. Seven items were identified as having the most robust employment inquiry and were rated a Level 3. Items were detailed and included questions such as whether an individual has frequent noise or dermatologic exposures at their place of employment, or feels they have control over their work situation.

Six of the seven items rated a Level 3 were identified within the Employment Exposure domain (Table 2). Screening tool items that assess Social Risk or Social Need domains disproportionately lacked context, with most falling into Levels 1 and 2 of contextualization. In contrast, most items that assess Employment Exposure were highly contextualized (Level 3).

Table 2 Item level of contextualization by question domain
Full size table

Discussion

Tools available to screen for employment in health systems are diverse in content and implementation, but relatively few assess the complex nature of work. Results from our analysis suggest that employment items in screening tools that have been evaluated in the included studies are underdeveloped and unclear in their intended purpose. In our content analysis, we first characterized the intent of the question, which we categorized into four Domains: Social Risk Factor, Social Need, Employment Exposure, and Legal Need. Half of the employment questions fell into the Social Need domain, which assessed respondents’ perceptions of employment as a concern, or which tied the question to an intervention. This Domain is similar to the Social Risk Factor domain, which assessed employment status in terms of whether the respondent was employed or what broad category of employment their work fell into (e.g., full-time vs. part-time work) without assessing the respondents’ need for support. In both domains, the purpose behind the question was unclear. If a respondent indicates that employment is a concern, does this suggest that they are concerned about finances, health insurance, or social support? Or, on the other hand, is their employment causing physical or psychological stress? Questions that fell into the Employment Exposure and Legal Need domains were clearer in their intent and more easily linked to a core measurement purpose (e.g., occupational exposure, environmental exposure, work characteristics, legal benefits, the legality of work arrangements).

Next, we examined the question and response options together and characterized the extent to which the item captures a respondent’s employment context. Together with the question Domain, this helps us understand how actionable a given item is. With more than half of the items assessed having little to no context (Level 1), our results suggest ambiguity in how screening tool questions are being developed, applied, and evaluated. Items without any employment context (e.g., “Are you employed? yes/no”) may stifle the potential benefits that screening tools can provide and have implications for how a healthcare delivery system can intervene to address the need. Even items that provided somewhat more employment context (Level 2) did not capture information specific enough for a provider to adjust one’s care plan or connect patients with resources [15]. For example, there was not enough information in these items glean details that would aid providers in discussing strategies to mitigate occupational risks based on an individual’s health status or help them make appropriate referrals to social services based on an individual’s need. In contrast, items identified as highly contextualized, with a Level 3, captured nuances that would allow providers to address specific aspects of an individual’s work context.

Of the tools identified that were highly contextualized, more than half were implemented in specialized healthcare delivery settings like musculoskeletal clinics and obstetric clinics. Providers in specialized settings may better understand the nuances of how employment status, working conditions, and occupational exposures impact their patients’ health. As such, providers in these settings are likely more incentivized to address employment in a contextual way to (1) better understand how best to treat a patient and (2) better identify measures of association between employment characteristics and health outcomes, which may be less apparent in primary care settings. Findings may also suggest that items of a tool reflect the level of impact the providers feel they have in modifying or addressing employment characteristics. For example, items in the Employment Exposure domain were mostly developed with an occupational health lens, while items in the Social Risk Factor and Social Need domains were more general. Occupational health providers may feel better equipped to understand how employment characteristics influence health and may feel more comfortable intervening to address a patient’s need. More exploratory evidence that looks at who developed the items, the primary intent, and the perceived modifiable risk would provide further insight into the relationship between contextualization and domain.

In addition to providing more concrete guidance for intervening, highly contextualized items with a clear purpose may encourage patient engagement if patients better understand how answering social screening questions are linked to solutions. While many patients believe that screening for social needs is valuable, patients need to be convinced that the screening tool items are intentional and to understand how providers will use the information [70, 71]. Future research should focus on developing items with a keener eye towards what context to include to adequately assess relevant outcomes of interest. Centering the intent of the item and considering the applicable contexts would provide a foundation for researchers to assess the effectiveness of individual questions more adequately. Implementation science frameworks, like the health equity implementation framework [72], would be particularly useful to describe how variation in question intent or clinical healthcare delivery setting might influence the effectiveness of screening tools given a particular setting or population.

Our analysis further examined the healthcare setting in which the screening tool has been used. Basic, less contextualized items might suffice for a general adult population where a wide range of social risks or social needs may be present, and the impact of work-related exposures could be less pronounced. However, our results suggest that specific populations may benefit from screening tool items better tailored to their needs. For example, pregnant, pediatric, and Veteran populations frequently appeared in our analysis, with each having different considerations and needs for care. Individuals in populations such as these could be at risk for more acute health implications warranting work adjustments or may more often experience working conditions that directly link to health. Distinguishing between settings is especially important in generating generalizable evidence across clinical healthcare delivery systems.

This study sought to identify and characterize employment- and work-related questions in screening tools implemented in clinical healthcare delivery systems. The observed variability in setting, domain, and level of contextualization suggests that it is challenging to assess the effectiveness of employment-related screening items and its direct impact on patient outcomes. The study had little insight into how individual screening tools were implemented and thus was limited in assessing the comparative effectiveness across the three content areas. Additionally, though patient health outcomes and clinical healthcare delivery system measures are critically important to capture in social needs interventions, like screening for employment, there is a lack of studies that assess the context of the screening tool and include common health and healthcare utilization outcomes. Therefore, there are few direct links that would allow this study to draw conclusions on the effectiveness of the screening tool on patient health and clinical healthcare delivery system outcomes.

Future research should consider the fragmented evidence available for interventions that bridge social and medical care. Assessing the setting, level of contextualization, and domains of screening tool items would provide other mechanisms to study comparative effectiveness and generate insight for providers to better tailor screening tools for patient care. The robust evidence connecting social determinants of health and health outcomes suggest that effectiveness research should explore outcomes beyond traditional measures of health and healthcare utilization. Including other measures of effectiveness such as short-term social needs, health-related behaviors, and quality of life could contribute to a better understanding of the impact of implementing screening tools.

Conclusions

Clinical healthcare delivery systems have a substantial opportunity to adopt and leverage screening tool items to address social determinants of health. For a social factor, like employment, questions in screening tools must be clear about the purpose and consider the context—individual and setting—of implementation. In order to maximize the utility of these tools, clinical healthcare delivery systems should carefully consider why they are asking the question, who is being asked, how screening responses will help to address the need, and how success will be evaluated. Efforts to do so will influence the accuracy of identifying and assessing employment as a social determinant of health and provide a landscape for evaluative work to develop best practices.

Data availability

The dataset analyzed for the content analysis is available from the corresponding author. All literature included was obtained from publicly available sources.

Abbreviations

SIREN:

Social Interventions and Research & Evaluation Network

References

  1. Wipfli BP, Wild SMPH, Richardson DMD, Hammer LP. Work as a Social Determinant of Health: a Necessary Foundation for Occupational Health and Safety. J Occup Environ Med. 2021;63(11):e830–3.

    Article  PubMed  Google Scholar 

  2. Ahonen EQ, Fujishiro K, Cunningham T, Flynn M. Work as an inclusive part of Population Health Inequities Research and Prevention. Am J Public Health. 2018;108(3):306–11.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lipscomb HJ, Loomis D, McDonald MA, Argue RA, Wing S. A conceptual model of work and health disparities in the United States. Int J Health Serv. 2006;36(1):25–50.

    Article  PubMed  Google Scholar 

  4. Ahmad F, Skinner HA, Stewart DE, Levinson W. Perspectives of family physicians on computer-assisted health-risk assessments. J Med Internet Res. 2010;12(2):e12.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Benach J, Vives A, Tarafa G, Delclos C, Muntaner C. What should we know about precarious employment and health in 2025? Framing the agenda for the next decade of research. Int J Epidemiol. 2016;45(1):232–8.

    Article  CAS  PubMed  Google Scholar 

  6. Lewchuk W, de Wolff A, King A, Polanyi M. From job strain to employment strain: Health effects of Precarious Employment. Just Labour. 2003;3.

  7. Van Aerden K, Puig-Barrachina V, Bosmans K, Vanroelen C. How does employment quality relate to health and job satisfaction in Europe? A typological approach. Soc Sci Med. 2016;158:132–40.

    Article  PubMed  Google Scholar 

  8. van der Noordt M, H IJ, Droomers M, Proper KI. Health effects of employment: a systematic review of prospective studies. Occup Environ Med. 2014;71(10):730–6.

    Article  PubMed  Google Scholar 

  9. Browne J, McCurley JL, Fung V, Levy DE, Clark CR, Thorndike AN. Addressing Social determinants of Health identified by systematic screening in a Medicaid Accountable Care Organization: a qualitative study. J prim care Community Health. 2021;12(101518419):2150132721993651.

    PubMed  PubMed Central  Google Scholar 

  10. Fujishiro K, Xu J, Gong F. What does occupation represent as an indicator of socioeconomic status? Exploring occupational prestige and health. Soc Sci Med. 2010;71(12):2100–7.

    Article  PubMed  Google Scholar 

  11. Toward Addressing Social Determinants of Health. A Health Care System Strategy. The Permanente Journal - Kaiser Permanente. 2020.

  12. Grus I, Bunce A, Davis J, Dambrun K, Cottrell E, Gold R. Initiating and Implementing Social Determinants of Health Data Collection in Community Health Centers. Popul Health Manag. 2021;24(1):52–8.

    Article  Google Scholar 

  13. Naz A, Rosenberg E, Andersson N, Labonte R, Andermann A, Collaboration C. Health workers who ask about social determinants of health are more likely to report helping patients: mixed-methods study. Can Fam Physician. 2016;62(11):e684–93.

    PubMed  PubMed Central  Google Scholar 

  14. Andermann A. Screening for social determinants of health in clinical care: moving from the margins to the mainstream. Public Health Rev. 2018;39:19.

    Article  PubMed  PubMed Central  Google Scholar 

  15. National Academies of Sciences E, Medicine, Health, Medicine D, Board on Health Care S, Committee on Integrating Social Needs Care into the Delivery of. Health Care to Improve the Nation’s H. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation’s Health. Washington (DC): National Academies Press (US) Copyright 2019 by the National Academy of Sciences. All rights reserved.; 2019.

  16. De Marchis E, Brown E, Aceves B, Loomba V, Molina M, Cartier Y, et al. State of the Science on Social Screening in Healthcare Settings. San Francisco, CA:: Social Interventions Research and Evaluation Network; 2022.

    Google Scholar 

  17. Gottlieb L, Fichtenberg C, Alderwick H, Adler N. Social determinants of Health: what’s a Healthcare System to do? J Healthc Manag. 2019;64(4):243–57.

    PubMed  Google Scholar 

  18. Taylor LA, Tan AX, Coyle CE, Ndumele C, Rogan E, Canavan M, et al. Leveraging Social Determinants Health: What Works? PLoS ONE. 2016;11(8):e0160217.

    PubMed  Google Scholar 

  19. Karran EL, A GC, Barker T, Chiarotto MAB, Dewidar A. The ‘what’ and ‘how’ of screening for social needs in healthcare settings: a scoping review. PeerJ. 2023;11:e15263.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Moen M, Storr C, German D, Friedmann E, Johantgen M. A review of tools to Screen for Social Determinants of Health in the United States: a practice brief. Popul Health Manag. 2020;23(6):422–9.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yan AF, Chen Z, Wang Y, Campbell JA, Xue QL, Williams MY, et al. Effectiveness of Social needs screening and interventions in clinical settings on utilization, cost, and clinical outcomes: a systematic review. Health Equity. 2022;6(1):454–75.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Benach J, Muntaner C, Solar O, Santana V, Quinlan M. Introduction to the WHO Commission on Social Determinants of Health Employment Conditions Network (EMCONET) study, with a glossary on employment relations. Int J Health Serv. 2010;40(2):195–207.

    Article  PubMed  Google Scholar 

  23. Artiga S, Hinton E. Beyond health care: the role of social determinants in promoting health and health equity. San Francisco, CA: Henry J. Kaiser Family Foundation; 2018.

    Google Scholar 

  24. Pinto AD, Hassen N, Craig-Neil A. Employment interventions in Health settings: a systematic review and synthesis. Ann Fam Med. 2018;16(5):447–60.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Goodman JM, Colon M, Cottrell Erika K, Guise J-M. Work as a social risk factor in pregnancy: A systematic review of screening practices related to working conditions and family leave among pregnant adults. Birth.n/a(n/a).

  26. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Alderwick H, Gottlieb LM. Meanings and misunderstandings: a Social Determinants of Health Lexicon for Health Care Systems. Milbank Q. 2019;97(2):407–19.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Albright DL, Johnson K, Laha-Walsh K, McDaniel J, McIntosh S. Social determinants of Opioid Use among patients in rural primary care settings. Social Work Public Health. 2021;36(6):723–31.

    Article  Google Scholar 

  29. Post Sennehed C, Gard G, Holmberg S, Stigmar K, Forsbrand M, Grahn B. Blue flags, development of a short clinical questionnaire on work-related psychosocial risk factors - a validation study in primary care. BMC Musculoskelet Disord. 2017;18(1):318.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Goldfeld S, D’Abaco E, Bryson H, Mensah F, Price AM. Surveying social adversity in pregnancy: the antenatal risk burden experienced by Australian women. J Paediatr Child Health. 2018;54(7):754–60.

    Article  PubMed  Google Scholar 

  31. Price AM, Bryson HE, Mensah F, Kemp L, Bishop L, Goldfeld S. The feasibility and acceptability of a population-level antenatal risk factor survey: cross-sectional pilot study. J Paediatr Child Health. 2017;53(6):572–7.

    Article  PubMed  Google Scholar 

  32. Cohen-Silver J, Laher N, Freeman S, Mistry N, Sgro M. Family fIRST, an interactive risk Screening Tool for families in a School-based Pediatric Clinic: a look at feasibility and Pilot Data. Clin Pediatr. 2017;56(3):217–25.

    Article  Google Scholar 

  33. Fleegler EW, Lieu TA, Wise PH, Muret-Wagstaff S. Families’ Health-related social problems and missed Referral opportunities. Pediatrics. 2007;119(6):e1332–41.

    Article  PubMed  Google Scholar 

  34. Neadley K, McMichael G, Freeman T, Browne-Yung K, Baum F, Pretorius E et al. Capturing the social determinants of health at the individual level: a pilot study. Public Health Res Pract. 2021;31(2).

  35. Ganguli I, Orav EJ, Weil E, Ferris TG, Vogeli C. What do high-risk patients value? Perspectives on a Care Management Program. J GEN INTERN MED. 2018;33(1):26–33.

    Article  PubMed  Google Scholar 

  36. Berkowitz SA, Hulberg AC, Hong C, Stowell BJ, Tirozzi KJ, Traore CY, et al. Addressing basic resource needs to improve primary care quality: a community collaboration programme. BMJ Qual Saf. 2016;25(3):164–72.

    Article  PubMed  Google Scholar 

  37. Berkowitz SA, Hulberg AC, Standish S, Reznor G, Atlas SJ. Addressing Unmet Basic Resource needs as part of Chronic Cardiometabolic Disease Management. JAMA Intern Med. 2017;177(2):244–52.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Sripada RK, Henry J, Yosef M, Levine DS, Bohnert KM, Miller E, et al. Occupational functioning and employment services use among VA primary care patients with posttraumatic stress disorder. Psychol Trauma. 2018;10(2):140–3.

    Article  PubMed  Google Scholar 

  39. Morgan KJ, Eastwood JG. Social determinants of maternal self-rated health in South Western Sydney, Australia. BMC Res Notes. 2014;7(101462768):51.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Gottlieb L, Hessler D, Long D, Amaya A, Adler N. A Randomized Trial on Screening for Social Determinants of Health: the iScreen Study. Pediatrics. 2014;134(6):e1611–8.

    Article  PubMed  Google Scholar 

  41. Mason J, Edlow M, Lear M, Scoppetta S, Walther V, Epstein I, et al. Screening for psychosocial risk in an urban prenatal clinic population: a retrospective practice-based research study. Soc Work Health Care. 2001;33(3):33–52.

    CAS  PubMed  Google Scholar 

  42. Thompson JN, Brodkin CA, Kyes K, Neighbor W, Evanoff B. Use of a questionnaire to improve occupational and environmental history taking in primary care physicians. J Occup Environ Med. 2000;42(12):1188–94.

    Article  CAS  PubMed  Google Scholar 

  43. Careyva BA, Hamadani R, Friel T, Coyne CA. A Social needs Assessment Tool for an urban latino Population. J Community Health. 2018;43(1):137–45.

    Article  PubMed  Google Scholar 

  44. Ridgeway JL, Beebe TJ, Chute CG, Eton DT, Hart LA, Frost MH, et al. A brief patient-reported outcomes Quality of Life (PROQOL) Instrument to Improve Patient Care. PLoS Med. 2013;10(11):e1001548.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Kusnoor SV, Koonce TY, Hurley ST, McClellan KM, Blasingame MN, Frakes ET, et al. Collection of social determinants of health in the community clinic setting: a cross-sectional study. BMC Public Health. 2018;18(1):550.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Sandhu S, Xu J, Blanchard L, Eisenson H, Crowder C, Munoz VS, et al. A Community Resource Navigator Model: utilizing student volunteers to Integrate Health and Social Care in a Community Health Center setting. Int J Integr Care. 2021;21(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Tou LC, Prakash N, Jeyakumar SJ, Ravi S. Investigating Social Determinants of Health in an Urban Direct Primary Care Clinic. Cureus. 2020;12(10):e10791.

    PubMed  PubMed Central  Google Scholar 

  48. Drake C, Eisenson H. Assessing and addressing social needs in primary care. NEJM Catalyst. 2019;5(6).

  49. Weir RC, Proser M, Jester M, Li V, Hood-Ronick CM, Gurewich D. Collecting Social Determinants of Health Data in the clinical setting: findings from National PRAPARE implementation. J Health Care Poor Underserved. 2020;31(2):1018–35.

    Article  PubMed  Google Scholar 

  50. Razani N, Long D, Hessler D, Rutherford GW, Gottlieb LM. Screening for Park Access during a primary care social determinants screen. Int J Environ Res Public Health. 2020;17(8).

  51. Reves SR, O’Neal JP, Gonzalez MM, McHenry C, Favour M, Etz RS. A 60-Second Survey to identify patients’ unmet social needs. Ann Fam Med. 2019;17(3):274.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Schwartz DA, Wakefield DS, Fieselmann JF, Berger-Wesley M, Zeitler R. The occupational history in the primary care setting. Am J Med. 1991;90(1):315–9.

    Article  CAS  PubMed  Google Scholar 

  53. Semple-Hess JE, Pham PK, Cohen SA, Liberman DB. Community Resource needs Assessment among families presenting to a Pediatric Emergency Department. Acad Pediatr. 2019;19(4):378–85.

    Article  PubMed  Google Scholar 

  54. Sokol RL, Mehdipanah R, Bess K, Mohammed L, Miller AL. When families do not request help: assessing a Social determinants of Health Screening Tool in Practice. J Pediatr Health Care. 2021;35(5):471–8.

    Article  PubMed  Google Scholar 

  55. Agarwal G, Pirrie M, Edwards D, Delleman B, Crowe S, Tye H, et al. Legal needs of patients attending an urban family practice in Hamilton, Ontario, Canada: an observational study of a legal health clinic. BMC Fam Pract. 2020;21(1):267.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Wylie SA, Hassan A, Krull EG, Pikcilingis AB, Corliss HL, Woods ER, et al. Assessing and referring adolescents’ health-related social problems: qualitative evaluation of a novel web-based approach. J Telemed Telecare. 2012;18(7):392–8.

    Article  PubMed  Google Scholar 

  57. de la Buitron P, Losi S, Sprague Martinez L, Bovell-Ammon A, Garg A, James T, et al. Implementing an EHR-based Screening and Referral System to Address Social Determinants of Health in Primary Care. Med Care. 2019;57(230027):S133–9.

    Article  Google Scholar 

  58. Tong ST, Liaw WR, Kashiri PL, Pecsok J, Rozman J, Bazemore AW, et al. Clinician experiences with screening for Social needs in primary care. J Am Board Fam Med. 2018;31(3):351–63.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Tsai J, Jenkins D, Lawton E. Civil Legal Services and medical-legal partnerships needed by the Homeless Population: A National Survey. Am J Public Health. 2017;107(3):398–401.

    Article  PubMed  PubMed Central  Google Scholar 

  60. van Beukering M, Velu A, Schonewille LHN, Duijnhoven R, Mol BW, Brand T et al. Evaluation of a blended care programme for caregivers and working pregnant women to prevent adverse pregnancy outcomes: an intervention study. Occup Environ Med. 2021(9422759).

  61. Garg A, Butz AM, Dworkin PH, Lewis RA, Thompson RE, Serwint JR. Improving the management of family psychosocial problems at low-income children’s well-child care visits: the WE CARE Project. Pediatrics. 2007;120(3):547–58.

    Article  PubMed  Google Scholar 

  62. Garg A, Sarkar S, Marino M, Onie R, Solomon BS. Linking urban families to community resources in the context of pediatric primary care. Patient Educ Couns. 2010;79(2):251–4.

    Article  PubMed  Google Scholar 

  63. Garg A, Toy S, Tripodis Y, Silverstein M, Freeman E. Addressing Social Determinants of Health at Well Child Care visits: a cluster RCT. Pediatrics. 2015;135(2):e296–304.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Herrera C-N, Brochier A, Pellicer M, Garg A, Drainoni M-L. Implementing Social Determinants of Health Screening at Community Health Centers: Clinician and Staff perspectives. J prim care Community Health. 2019;10(101518419):2150132719887260.

    PubMed  PubMed Central  Google Scholar 

  65. Parker MG, Garg A, Brochier A, Rhein LM, Forbes ES, Klawetter S et al. Approaches to addressing social determinants of health in the NICU: a mixed methods study. J Perinatol. 2020:1–9.

  66. Spencer AE, Baul TD, Sikov J, Adams WG, Tripodis Y, Buonocore O, et al. The relationship between Social risks and the Mental Health of School-Age Children in Primary Care. Acad Pediatr. 2020;20(2):208–15.

    Article  PubMed  Google Scholar 

  67. Page-Reeves J, Kaufman W, Bleecker M, Norris J, McCalmont K, Ianakieva V, et al. Addressing Social Determinants of Health in a clinic setting: the WellRx Pilot in Albuquerque, New Mexico. J Am Board Fam Med. 2016;29(3):414–8.

    Article  PubMed  Google Scholar 

  68. Wiegner L, Hange D, Bjorkelund C, Ahlborg G. Jr. Prevalence of perceived stress and associations to symptoms of exhaustion, depression and anxiety in a working age population seeking primary care–an observational study. BMC Fam Pract. 2015;16(100967792):38.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Zachek CM, Schwartz JM, Glasser M, DeMicco E, Woodruff TJ. A screening questionnaire for occupational and hobby exposures during pregnancy. Occup Med. 2019;69(6):428–35.

    Article  CAS  Google Scholar 

  70. Byhoff E, De Marchis EH, Hessler D, Fichtenberg C, Adler N, Cohen AJ, et al. Part II: a qualitative study of Social Risk Screening acceptability in patients and caregivers. Am J Prev Med. 2019;57(6):S38–46.

    Article  PubMed  PubMed Central  Google Scholar 

  71. De Marchis EH, Hessler D, Fichtenberg C, Adler N, Byhoff E, Cohen AJ, et al. Part I: a quantitative study of Social Risk Screening acceptability in patients and caregivers. Am J Prev Med. 2019;57(6):S25–37.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Woodward EN, Singh RS, Ndebele-Ngwenya P, Melgar Castillo A, Dickson KS, Kirchner JE. A more practical guide to incorporating health equity domains in implementation determinant frameworks. Implement Sci Commun. 2021;2(1):61.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors are grateful to Jeanne-Marie Guise and Erika K. Cottrell for their support developing the systematic review on which this study is based. They would also like to thank Andrew Hamilton, M.S./M.L.S., for his support in developing and implementing our database searches.

Funding

This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award K12HD043488. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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    Mina Colon & Julia M. Goodman

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Mina Colon and Julia M Goodman collected, analyzed, and interpreted the data. All authors contributed to the article’s content and approved the final manuscript.

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Colon, M., Goodman, J.M. Screening tools for employment in clinical healthcare delivery systems: a content analysis. BMC Health Serv Res 24, 720 (2024). https://doi.org/10.1186/s12913-024-10976-3

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