Modernising physician resource planning: a national interactive web platform for Canadian medical trainees
Bibliographic record
Abstract
Abstract Background: Healthcare systems rely heavily upon human resources to ensure high-quality access to care for the general population. With significant health worker shortages predicted worldwide in the coming decades, maximizing the current workforce by means of a physician resource planning (PRP) strategy that ensures the right number, mix, and distribution of physicians to meet population needs is warranted. In Canada, there is an insufficient number of primary care providers, and disproportionately low numbers of specialist physicians in rural compared to urban regions. Currently, Canadian medical students are not effectively included in PRP strategy and lack the required information for career orientation to help rebalance the population’s workforce needs. This paper aims to present the Health Human Resource (HHR) Platform, a comprehensive web tool that includes relevant workforce data to empower medical students in choosing a discipline based on both personal interests and social accountability.Methods: Physician workforce data were amalgamated from national public agencies. Comments from Canadian residency program directors and useful resources for career planning were collected by the Canadian Federation of Medical Student’s (CFMS) HHR Task Force. This information was consolidated to create the HHR Platform using a DigitalOcean server. The backend database utilizes MySQL, while the frontend utilizes React and Material UI, with additional aspects integrated through Leaflet and Google Charts. Results: A national interactive platform was created that uses a map, comparison table, and trends graph to illustrate over 500,000 unique data points from 37 datasets, including specific information and resources spanning 62 medical specialties from 2015 onwards. There was a 24.6% response rate for program director comments. During the first four months of the HHR Platform launch, there were 2463 different users, of which 998 were returning, with an average of 20.2 users per day spending on average 3 minutes and 4 seconds on the platform.Conclusions: The HHR Platform constitutes a bottom-up national approach to PRP informing medical students on the mix and distribution of physicians needed for a better alignment with residency positions, and ultimately meet the future healthcare demands of the Canadian population.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".