Assessing the learning needs of physical medicine and rehabilitation residents to develop a geriatric medicine and rehabilitation curriculum
Bibliographic record
Abstract
BACKGROUND: Older adults with functional impairment are cared for by physiatrists in rehabilitation, but physiatrist training in geriatric-related competencies remains suboptimal. To develop a geriatric rehabilitation (GR) curriculum and explore opportunities for improvement, a needs assessment of stakeholders was conducted to understand physical medicine and rehabilitation (PMR) residents' comfort levels and learning needs in geriatrics. METHODS: A mixed-methods design was employed. PMR residents (n = 18) and practicing physiatrists (n = 40) completed a questionnaire; and PMR residents, physiatrists and key informants (n = 9; n = 4; n = 6) participated in focus groups and semi-structured interviews to explore geriatric experiences of trainees and educational needs in geriatrics and rehabilitation. Data were qualitatively analyzed using constructivist-grounded theory. RESULTS: Residents and physiatrists highlighted similar topics as areas of low comfort in knowledge. Interviews prioritized critical geriatric topics (gait assessment, falls, cognitive impairment, movement disorders, and polypharmacy) and highlighted disposition planning and end-of-life care as areas needing further curriculum support. Challenges in delivering geriatric education were also identified. CONCLUSION: What emerged from the needs assessment was a series of critical geriatric educational priorities for the development of a GR curriculum for physiatry trainees - arising at an opportune time given the shift toward competency-based residency education.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".