Teaching health systems leadership and innovation to physicians
Bibliographic record
Abstract
BACKGROUND: A master's programme in Health Systems Leadership and Innovation was launched in 2016 to integrate health systems science and innovation management within the medical education continuum. OBJECTIVES: To identify faculty and staff perceptions of tailoring the programme to accommodate potential future learning needs as a continuous quality improvement initiative of the programme. METHODS: A combination of two qualitative research methodologies was used: (1) a situational analysis to explain context and (2) a collaborative autoethnographic approach to understand the evolution of the programme and future directions. Faculty and staff involved with the programme were invited to participate after obtaining institutional research ethics approval. In conducting a collaborative autoethnography, all authors are participants who narrate, analyse and theorise about their individual and or collective experiences. RESULTS: Nine faculty and three staff members narrated their perceptions of the programme. The situational analysis identified major internal and external actors, major processes and external actants relevant to the programme. It also outlined the multiple overlapping social arenas where the students, faculty and staff were situated through a social world map and differing positions of the authors with respect to the programme's future learners. The master narrative identified an urgent need for internal and external communications about the programme and to revisit course delivery methods. The authors were divided in their opinion as to whether the programme should continue to cater to undergraduate medical students or focus on physicians or have learners from multiple educational levels in the same class. CONCLUSIONS: The programme needs marketing, continuous course assessments and revisions to ensure visibility and relevance. The programme offers a flexible pathway for students at different stages in the career path from novice medical students to consultant physicians, and tensions related to the level of medical education hierarchy in the class are being managed by the faculty.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".