Assessing the learning needs of physical medicine and rehabilitation residents to develop a geriatric medicine and rehabilitation curriculum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it