Experiential learning, collaboration and reflection: key ingredients in longitudinal faculty development
Bibliographic record
Abstract
BACKGROUND: Longitudinal faculty development (LFD) may allow for increased uptake of teaching skills, especially in a forum where teachers can reflect individually and collectively on the new skills. However, the exact processes by which such interventions are effective need further exploration. METHODS: This qualitative study examined an LFD initiative teaching a novel feedback approach attended by five family practice physicians. The initiative began with two 1.5-hour workshops: Goal-Oriented Feedback (as the teaching skill to be developed) and Narrative Reflection (as the tool to support personal reflection on the skill being learned). Over the subsequent six-months, the five participants iteratively applied the feedback approach in their teaching and engaged in narrative reflection at four 1-hour group sessions. Transcripts from the group discussions and exit interviews were analyzed using thematic analysis. RESULTS: Iteratively trialing, individually reflecting on, and collectively exploring efforts to implement the new feedback approach resulted in the development of a learning community among the group. This sense of community created a safe space for reflection, while motivating ongoing efforts to learn the skill. Individual pre-reflection prepared individuals for group co-reflection; however, written narratives were not essential. CONCLUSION: LFD initiatives should include an emphasis on ensuring opportunities for iterative attempts of teaching skills, guided self-reflection, and collaborative group reflection and learning to ensure sustainable change to teaching practices.
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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 | 0.140 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".