Continuous Quality Improvement and Community-Based Faculty Development through an Innovative Site Visit Program at One Institution
Bibliographic record
Abstract
This article describes and evaluates a unique site-visit process for community-based teaching sites. A continuous quality-improvement program was developed by the undergraduate program in the Department of Family and Community Medicine at the University of Toronto Faculty of Medicine to facilitate and document both self- and peer-assessment. A pilot program was launched in 2000, and, after some adjustments based on initial feedback, the program in its current form was implemented in 2002. This program provides individualized support mechanisms to address the faculty development needs and infrastructure requirements of community-based, mostly volunteer, teachers. It also trains participating reviewers to provide individualized faculty development at the point of teaching. During their training, reviewers receive a toolkit consisting of suggestions for initial contact with teachers, guidelines for peer assessments, previously completed previsit teacher surveys, reviewer checklists, postvisit feedback forms, sample thank-you letters, and a faculty development reference resource list. A two-year evaluation of the program demonstrated that faculty and reviewer participants perceived it to be comprehensive, consistent, informative, and an acceptable method of reviewing existing and prospective community-based teaching sites. This program should be transferable to other institutions that engage in community-based teaching.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".