Assessing Family Medicine Residents’ Preparedness for the Practice of Geriatric Care
Bibliographic record
Abstract
INTRODUCTION: Family physicians provide care to a growing and often complex group of older adults. Family medicine residencies are expected to prepare trainees to manage the medical and social needs of these seniors, but whether they feel prepared to do so is unknown. This study aimed to assess family medicine residents' sense of preparedness to address geriatric-related issues, and to identify factors that might affect this self-evaluation. METHODS: As an exploratory cross-sectional study, a 28-item questionnaire was circulated among 180 McGill University family medicine residents for voluntary completion. The survey assessed residents' demographics, attitudes toward, and perceptions of their training in geriatrics. RESULTS: One hundred residents (55.5% response rate) completed the questionnaire. Residents perceived their level of preparedness increasing as they progressed in their training; however, they felt least comfortable managing behavioral problems and engaging community resources. Positive perceptions about their training for geriatric care was proportionate to the number of positive role models they had during training. Sense of preparedness was most strongly and significantly associated with the duration of exposure residents had to long-term care. CONCLUSION: The aging population requires physicians who are interested in care of the elderly and who are adequately prepared through comprehensive training to care for the complex medical and social issues that affect this large segment of society. This study, through its survey of family medicine residents, provides insights about potential curriculum changes and recommendations for updating the approach to care of the elderly.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".