Do organizational cultures of Canadian medical schools promote a quality culture?
Bibliographic record
Abstract
Purpose: Medical schools are expected to engage in ongoing reflection to maintain the quality of the education they deliver, that is, to cultivate a Quality Culture. Quality Culture integrates the culture of an organization with its structure and management processes. The culture of medical schools has not been previously studied. Organizational cultures can be identified using the Competing Value Questionnaire, and classified into four types, based on organizations’ climate, leader style, reward systems, and strategic emphasis. Clan and Open cultures are typically positively associated with quality improvement. This study identifies the dominant organizational cultures of Canadian medical schools. Method: Sixteen of the 17 Canadian medical schools were invited to participate; one school was excluded due to ongoing accreditation activities. Faculty members of participating schools were surveyed. Results: Eleven (69%) schools participated. Nine had a dominant Hierarchical culture; two had a dominant Clan culture. Conclusions: Only two schools had a Clan culture, which might better support ongoing reflections on quality improvement. Schools leaders should examine the staff climate, leadership style, rewards system, and strategic emphasis in place at their school; these will provide clues to the existing culture and help prioritize changes required to support the implementation of a Quality Culture.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".