Not enough doctors or not enough needs? Refocusing health workforce planning from providers and services to populations and needs
Bibliographic record
Abstract
The importance of allocating services in accordance with population needs is well-established. Needs-based approaches to geographical resource allocation were established in the National Health Service in the UK in the 1970s, but the role of population needs has not extended to planning for the quantity and mix of health care services or for the providers required to deliver these services. We present a framework that integrates health service and workforce planning focused on responding to population needs. Using data from the General Household Survey for England over the period 1985-2006, we illustrate trends in health needs and service use per capita. Despite needs per capita falling, service use has increased. Rates of increase in service use are greater among those with less needs illustrating that, in the absence of appropriate planning methods, increases in service use may result from supplier influence rather than policy decisions.
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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 | 0.035 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".