Linking General Internal Medicine Residency Training to Human Resource Needs and Roles in a Changing Health Landscape
Bibliographic record
Abstract
Recently, there have been frequent calls for more generalists in the health care system, including General Internal Medicine (GIM). At the same time, the Royal College of Physicians and Surgeons has published a report on unemployed and underemployed specialists throughout Canada. GIM residency programs aim to ensure all graduates have future employment positions that will benefit Canadians. However, there is currently little linkage between the educational and healthcare systems in terms of utilizing future health care needs to inform postgraduate training. There is a lack of consensus on how to plan future health care workforce needs. There is, however, consensus that this is important for both the population and for future physicians. Predictions must also take into account context, such as Saskatchewan's significantly rural and aboriginal population. Difficulties in health care workforce planning include economic factors, differences in physician scope of practice, and regional variations in scope of practice. To fully prepare graduates for both core GIM competencies and competencies tailored to their future practice, it is necessary for us to understand the range of scope of GIM practice in Saskatchewan. It is crucial to understand both current and anticipated perceived scopes of practice and practice opportunities for General Internists in order to plan physician resource needs and the required educational resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".