Health human resources planning and the production of health: development of an extended analytical framework for needs-based health human resources planning.
Bibliographic record
Abstract
Health human resources planning is generally based on estimating the effects of demographic change on the supply of and requirements for healthcare services. In this article, we develop and apply an extended analytical framework that incorporates explicitly population health needs, levels of service to respond to health needs, and provider productivity as additional variables in determining the future requirements for the levels and mix of healthcare providers. Because the model derives requirements for providers directly from the requirements for services, it can be applied to a wide range of different provider types and practice structures including the public health workforce. By identifying the separate determinants of provider requirements, the analytical framework avoids the "illusions of necessity" that have generated continuous increases in provider requirements. Moreover, the framework enables policy makers to evaluate the basis of, and justification for, increases in the numbers of provider and increases in education and training programs as a method of increasing supply. A broad range of policy instruments is identified for responding to gaps between estimated future requirements for care and the estimated future capacity of the healthcare workforce.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".