Protected block time for teaching and learning in a postgraduate family practice residency program.
Bibliographic record
Abstract
OBJECTIVE: To explore the elements necessary for a high-quality educational experience in a family practice residency program with respect to scheduling, learning environment, and approaches to teaching and learning. DESIGN: An interpretative, qualitative study using a generative-inquiry approach. SETTING: The Nanaimo Site of the University of British Columbia Family Practice Residency Program. PARTICIPANTS: Fifteen physician instructors and 16 first- and second-year residents. METHODS: Data were gathered from 2 qualitative focus group interviews with residents; 2 qualitative focus group interviews with physician instructors; and structured and semistructured observation of 2 in-class seminars, with a focus on residents' engagement with the class. Results were analyzed and categorized into themes independently and collectively by the researchers. MAIN FINDINGS: Protected block time for teaching and learning at the Nanaimo Site has been effective in fostering a learning environment that supports collegial relationships and in-depth instruction. Residents and physician instructors benefit from the week-long academic schedule and the opportunity to teach and learn collaboratively. Participants specifically value the connections among learning environment, collegiality, relationships, reflective learning, and the teaching and learning process. CONCLUSION: These findings suggest that strategic planning and scheduling of teaching and learning sessions in residency programs are important to promoting a comprehensive educational experience.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".