Learning in Faculty Development: The Role of Social Networks
Bibliographic record
Abstract
PURPOSE: Faculty development is increasingly acknowledged as an important aspect of health professions education. Its conceptualization has evolved from an individual skills training activity to contemporary notions that draw on an organizational model. This organizational model recognizes relationships and networks as important mediators of knowledge mobilization. Although such conceptual advancements are critical, we lack empirical evidence and robust insights into how social networks function to shape learning in faculty development. The purpose of this study was to understand how informal professional social networks influence faculty development learning in the health professions. METHOD: This study used a qualitative social network approach to explore how teaching faculty's relationships influenced their learning about teaching. The study was conducted in 2018 in an undergraduate course at a Canadian medical school. Eleven faculty participants were recruited, and 3 methods of data collection were employed: semistructured interviews, participant-drawn sociograms, and demographic questionnaires. RESULTS: The social networks of faculty participants influenced their learning about teaching in the following 4 dimensions: enabling and mobilizing knowledge acquisition, shaping identity formation, expressing vulnerability, and scaffolding learning. CONCLUSIONS: Faculty developers should consider faculty's degree of social embeddedness in their professional social networks, as our study suggests this may influence their learning about teaching. The findings align with recent calls to conceptually reorient faculty development in the health professions as a dynamic social enterprise.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".