Physical Medicine and Rehabilitation Residency Quality Improvement and Research Curriculum
Bibliographic record
Abstract
ABSTRACT: Physical medicine and rehabilitation residency programs do not demonstrate a uniform level of training and mentorship for resident scholarly activities related in part to variable utilization of standardized curricula. The aim of this study was to design, develop, implement, and evaluate a structured Quality Improvement and Research Curriculum for a physical medicine and rehabilitation residency program in academic year 2015 using standardized methodology. A combination of five-phase project-lifecycle and six-step medical-curriculum development methodologies was used to integrate existing resources into five institutional domains: (1) Patient Safety and Quality Improvement Program; (2) Research Mentorship Program; (3) Rehab in Review; (4) Publication and Presentation Resources, and (5) Research and QI Lecture Series. Dedicated resident-faculty teams were created for individual domains and for the overall curriculum. Written materials developed included scope documents, reporting forms, and tracking tables. A dedicated webpage on the department website served as an accessible resource. A bimonthly Updates newsletter highlighted ongoing resident achievements. Program and resident outcome metrics were evaluated at the mid and end of academic year 2015. Excellent resident and good faculty participation in the curriculum was observed. Resident publication and presentation productivity improved. Time was the biggest barrier to success. Key factors for success included phased implementation, dedicated teams, scope clarity, accessible resources, personnel support, resident champions, and faculty mentorship.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".