Examining the Geriatric Content of Canada’s Newest Undergraduate Medical Program: Are Graduates of the Northern Ontario School of Medicine Acquiring the Basic Competencies to Care for an Increasingly Aging Population?
Bibliographic record
Abstract
Inadequate numbers of physicians skilled at providing specialized care of the elderly, has initiated inquiry as to how medical schools will ensure tomorrow’s physicians are capable of providing the most appropriate care for Canada’s growing population of aging seniors. The Canadian Geriatrics Society has responded to such concerns with the establishment of recommended geriatric learning objectives. This thesis examined the geriatric content of the undergraduate curriculum of Canada’s newest medical school, the Northern Ontario School of Medicine, and compared these findings to the Canadian Geriatrics Society’s recommended ‘Core Competencies in the Care of Older Persons for Canadian Medical Students’. While there was a respectful compliance with the recommendations, findings reveal that five of the twenty recommended competencies were absent in the curriculum objectives. Further, present competencies were found to be unequally distributed across the curriculum in relation to both the year and the teaching setting. The results suggest areas for improvement as recommended competencies are intended as a minimum standard for performance in caring for the elderly.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".