FM POD: an evidence-based blended teaching skills program for rural preceptors.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The University of Calgary's Rural Integrated Community Clerkship anchors final-year medical student education in 9 months of family medicine. The purpose of this pilot study was to evaluate the Family Medicine Preceptor Online Development (FM POD) program, designed to meet the faculty development needs of rural preceptors facing challenges of geographical distribution and time constraints. METHODS: The theoretically based program used a blended learning approach, beginning with a face-to-face workshop to strengthen participants' social presence during online interactions to follow. Asynchronous narrated presentations supplied foundational knowledge prior to facilitated synchronous web conferences, where participants shared experiences and co-constructed new knowledge. The program was evaluated using mixed methods, including surveys and focus group discussion. RESULTS: Evaluation tools generated data with high internal consistency reliability; focus group information substantiated and enriched the quantitative survey data. Participants enjoyed collaborating with colleagues and rated their learning experiences highly, reporting meaningful and statistically significant increases in mean comfort with all the precepting skills taught: giving effective feedback, using questions to teach, teaching communications skills, helping learners in difficulty, and making teaching time-efficient. All effect sizes were large. Increased comfort with distance learning technologies was a positive consequence. CONCLUSIONS: Results support the applicability of principles of social constructivism, experiential learning, and reflective learning in these participants. The program was highly rated and effectively increased participants' comfort with teaching skills, offering practical off-the-peg modular faculty development in basic teaching skills for distributed faculty. Participants appreciated the flexible delivery format, which course developers found readily adaptable for additional topics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".