Exploring the culture of faculty development: insights from Canadian leaders of faculty development
Bibliographic record
Abstract
Background: Although the word culture is frequently mentioned in research on faculty development (FD), the concept is rarely explored. This research aimed to examine the culture of FD in Canada, through the eyes of leaders of FD in the health professions. Studying culture can help reveal the practices and implicit systems of beliefs and values that, when made explicit, could enhance programming. Method: FD leaders from all Canadian medical schools were invited to participate in semi-structured telephone interviews between November 2016 and March 2017. The researchers used a constructivist methodology and theoretical framework located within cultural studies, borrowing from phenomenological inquiry to move beyond descriptions to interpretations of participants' perceptions. Constant comparison was used to conduct a thematic analysis within and across participants' interview transcripts. Results: Fifteen FD leaders, representing 88% of medical schools (15/17) in Canada, participated in this study. Four themes characterized the culture of FD: balancing competing voices and priorities; cultivating relationships and networks; promoting active, practice-based learning; and negotiating recognition. Conclusion: Although the culture of FD may vary from context to context, this study revealed shared values, practices, and beliefs, focused on the continuous improvement of individual and collective abilities and the attainment of excellence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.033 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".