Predicting Future Physician Output for British Columbia, Canada
Bibliographic record
Abstract
<p><em>This study looks at data since 2002 and estimates a prediction for the health care output by physicians for British Columbia. The measure used to capture this output is full time equivalence (FTE), developed by the Canadian Institute for Health Information to capture an aggregate level of output by physicians through the value of their billings. The paper uses past data to estimate future physician numbers for the province based on Canadian medical school graduates, interprovincial migration, as well as estimates for the number of physicians leaving the workforce and the number of foreign educated physicians entering the province every year. Taking this prediction for future number of physicians, along with data on the age and gender distribution of doctors, BC population estimates, and previous FTE data, a regression model is developed to predict the level of FTE in BC for 2018 to 2020. This research ultimately predicts a steady, but modest rise in FTE for BC in the next few years. However, whether this growth will continue beyond 2020 is unclear, and a rise alone does not necessarily mean that it will better address future demand as BC is currently experiencing a shortage in physician services, and the demand for health care is expected to rise with the increasing proportion of seniors to working age individuals in the province. This paper suggests that changes should be put in place to increase the number of seats available in Canadian medical schools to address the shortage of physicians in the long term, and that BC will have to increase the number of foreign educated doctors in order to address shortages in the short term.</em></p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".