Medical Manpower Indicators: Are policymakers using poor surrogate indicators of access to Family Doctor services ?
Bibliographic record
Abstract
Medical Manpower Indicators: Are policymakers using poor surrogate indicators of access to Family Doctor services ? In 2012, the Quebec Ministry of Health and Social Services (MSSS) reported that the population of Montreal consumed the services of 1,663 family doctors (FTE consumed). The Ministry also reported that Montreal had 1,922 family doctors (FTE in place) practicing within its boundaries. The same year the Canadian Institute of Health Information (CIHI) reported that the region of Montreal had 2,454 family physicians. Medical manpower indicators are a key elements in the planning of most developed countries’ medical systems. It is therefore surprising that we see such great variations in their measurement. In this analysis we explore the differences in these measurements and their causes. Findings Headcount is a very poor surrogate indicator for use of Family Medicine Services It is not surprising that Headcounts poorly estimate the delivery of family physician services in the province, as it includes physicians who do not, in part or in full, engage in clinical practice. These physicians may be involved in administration, academia, research, public health or occupational health sectors. Some may be retired or working elsewhere. As expected, this reality most affects large urban areas and academic centers. It can also be said that Headcounts, which assign a physician to the region of his home address, are fairly irrelevant in very remote regions that may be adequately served by traveling physicians who primarily reside in larger cities. FTE in place is a poor surrogate indicator of use of family medicine services The discrepancy of up to 15.6 % was found between FTE in place and FTE consumed. This is entirely explained by migration of patients and warrants further investigation. Hence, it is not surprising that the greatest variation was noted in urban and suburban areas.
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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 | 0.031 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".