Building successful and sustainable academic health science partnerships: exploring perspectives of hospital leaders
Bibliographic record
Abstract
BACKGROUND: Clinical work-based internships form a key component of health professions education. Integral to these internships, academic health science partnerships (AHSPs) exist between universities and teaching hospitals. Our qualitative descriptive study explored the perspectives of hospital leadership on AHSPs: what they are composed of, and the facilitators and barriers to establishing and sustaining these partnerships. METHODS: Fifteen individuals in a variety of hospital leadership positions were purposively sampled to participate in face-to-face interviews, after which a thematic analysis was conducted. RESULTS: Participants reported that healthcare and hospital infrastructure shapes and constrains the implementation of clinical education. The strength of the hospitals' relationship with the medical profession facilitated the partnership, however other health professions' partnerships were viewed less favourably. Participants emphasized the value of hospital leaders prioritizing education. Further, our findings highlighted that communication, collaboration, and involvement are considered as both facilitators and barriers to active engagement. Lastly, opportunities stemming from the partnership were identified as research, current best practice, improved patient care, and career development. CONCLUSION: Our study found that AHSPs involve the drive of the university and hospitals to gain valued capital, or opportunities. Reciprocal communication, collaboration, and involvement are modifiable components that are integral to optimizing AHSPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".