Identifying the areas of low self-reported confidence of internal medicine residents in geriatrics: a descriptive study of findings from a structured geriatrics skills assessment survey
Bibliographic record
Abstract
BACKGROUND: Currently, no standardized methods exist to assess the geriatric skills and training needs of internal medicine trainees to enable them to become confident in caring for older patients. This study aimed to describe the self-reported confidence and training requirements in core geriatric skills amongst internal medicine residents in Toronto, Ontario using a standardized assessment tool. METHODS: This study used a novel self-rating instrument, known as the Geriatric Skills Assessment Tool (GSAT), among incoming and current internal medicine residents at the University of Toronto, to describe self-reported confidence in performing, teaching and interest in further training with regard to 15 core geriatric skills previously identified by the American Board of Internal Medicine. RESULTS: 190 (75.1%) out of 253 eligible incoming (Year 0) and current internal medicine residents (Years 1-3) completed the GSAT. Year 1-3 internal medicine residents who had completed a geriatric rotation reported being significantly more confident in performing 13/15 (P < 0.001 to P = 0.04) and in teaching 9/15 GSAT skills (P < 0.001 to P = 0.04). Overall, the residents surveyed identified their highest confidence in administering the Mini-Mental Status Examination and lowest confidence in assessing fall risk using a gait and balance tool, and in evaluating and managing chronic pain. CONCLUSION: A structured needs assessment like the GSAT can be valuable in identifying the geriatric training needs of internal medicine trainees based on their reported levels of self-confidence. Residents in internal medicine could further benefit from completing a mandatory geriatric rotation early in their training, since this may improve their overall confidence in providing care for the mostly older patients they will work with during their residency and beyond.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".