A mixed methods evaluation of a 4-week geriatrics curriculum in strengthening knowledge and comfort among orthopaedic surgery residents
Bibliographic record
Abstract
BACKGROUND: In 2014, the University of Toronto Faculty of Medicine implemented a 4-week "Orthogeriatrics" rotation for orthopaedic surgery residents. We sought to assess the rotation's impact on trainees' knowledge, attitudes, and behaviours toward caring for older adults, and explore areas for improvement. METHODS: We used a mixed methods concurrent triangulation design. The Geriatrics Clinical Decision-Making Assessment (GCDMA) and Geriatric Attitudes Scale (GAS) compared knowledge, attitudes, and behaviours between trainees who were or were not exposed to the curriculum. Rotation evaluations and semi-structured interviews with trainees and key informants explored learning experiences and the curriculum's impact on resident physician growth and development in geriatric competencies. RESULTS: Among trainees who completed the GCDMA (n = 19), those exposed to the rotation scored higher in knowledge compared to the unexposed cohort (14.4 ± 2.1 vs. 11.3 ± 2.0, p < 0.01). The following themes emerged from the qualitative analysis of 29 stakeholders: Increased awareness and comfort regarding geriatric medicine competencies, appreciation of the value of orthogeriatric collaboration, and suggestions for curriculum improvement. CONCLUSIONS: These results suggest that the Orthogeriatrics curriculum strengthens knowledge, behaviour, and comfort towards caring for older adults. Our study aims to inform further curriculum development and facilitate dissemination of geriatric education in surgical training programs across Canada and the world.
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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 | 0.057 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".