International Medical Workforce Collaborative Conference
Bibliographic record
Abstract
The aim of this paper is to provide an overview of documented attempts to implement needs-based health human resource planning in Canada and discuss an innovative needs-based approach to family physician planning in one Canadian province that facilitates the evaluation of policy options to address the gap between the supply of and requirements for family physicians. Background/Recent Research in Area Recently, there have been two exhaustive reviews of documented attempts at needs-based health human resource (HHR) planning worldwide (Tomblin Murphy et al., 2004, Tomblin Murphy et al., 2007a). One of the overall conclusions of both reviews was that HHR planning in Canada has tended to be based on utilization patterns, the supply of health care professionals, and/or budgetary capacity, rather than on the actual health care needs of the population or the health policies governing it. There are several limitations to this approach, two of which are that it does not account for trends in population health need or trends in provider productivity. Improvements in health status would reduce the total service requirements in the population and improvements in productivity would increase the supply of services from the stock of providers. If such trends were to unfold, the forecasts would overestimate the requirements for HHR, and if
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.231 | 0.081 |
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".