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Table 4 Summary of the criteria for operationalizing population need for healthcare for physician planning. (0/1) simplified indicates that the aspect was assessed as criterion being ‘present’ (1) or ‘not present’ (0). A full description can be found in the Additional file 2 NB: The number approaches result into n = 24 instead of n = 18 as three studies adopted several approaches

From: Assessing needs-based supply of physicians: a criteria-led methodological review of international studies in high-resource settings

1. Conceptual framework

Findings

1.1 Selection and justification of needs indicators

• Theoretical rationale (0/1)

• Empirical validation (0/1)

Theoretical rationale for the indicators

• n = 24

Empirical validation of indicators

• n = 5

1.2 Relationship between supply and need

• Potential influence (0/1)

• Potential unmet need or lack of physicians (0/1)

• Potential overuse or oversupply (0/1)

Discuss potential influence of supply

• n = 9

Adjust potential unmet need or lack of physicians

• n = 6

Adjust potential overuse or oversupply

• n = 0

2. Data basis

Findings

2.1 External validity

• Representativeness

Representativeness

• Population data: n = 2

• Representative sample: n = 2

• Convenience samples: n = 2

• Mixed data: n = 18

2.2 Internal validity

• Accuracy of indicators

Discuss accuracy of indicators

• n = 14

2.3 Timeliness and availability

• Survey period

Survey/recording periods (in years)

• Ranges between 1–20 years

3. Modelling and translation into physician capacity

Findings

3.1 Transformation into provider requirements

• Methodology

Methodology to translate estimated need into supply

• FTE: n = 14

• Physician-to-population ratio adjustment: n = 10

3.2 Model selection and validation

• Type of model

• Justification and validation (0/1)

Type of model

• Regression-based: n = 4

• Simulations: n = 9

• Extrapolations: n = 11

Validation of the model

• n = 21

3.3 Level of analysis

• Aggregated data (0/1)

• Individual data (0/1)

Model based on aggregated data

• n = 21

Model based on individual data

• n = 3

4. Integration of future trends and developments

Findings

4.1 Projection variables

• Selection of variables

Variables for projection models

• Demographics: n = 13

• Utilization: n = 5

• Supply: n = 5

• Morbidity: n = 3

• Insurance status: n = 2

• Health behavior: n = 1

4.2 Planning horizon

• Length

• Validation (0/1)

Length of need projections

• Ranges between 10–31 years

Validation of length

• n = 0