Embedding a Culture of Research in Canadian Community Hospitals
Bibliographic record
Abstract
This study aims to understand how research culture is experienced and shaped in Canadian community hospitals to provide strategies for strengthening community hospital research culture. This qualitative interpretive study employed Schein’s Model of Organizational Culture as a theoretical framework to identify the underlying components of research culture. Data was collected via semi-structured interviews and analyzed using reflexive thematic analysis. The final sample included 38 participants, representing 20 different community hospitals across 6 provinces. As community hospital research programs advanced, participants described a shift in research culture based on three key themes: Awareness and Engagement, Understanding the ‘Why’ and Leadership Support and Organizational Commitment. Aligning with each theme, recommended strategies to achieve an embedded culture of research involve six categories: communications; relationship building; mentorship, training and education opportunities; selecting locally relevant studies; system-level support; and ‘top-down’ versus ‘bottom-up’ approaches. These findings illustrate how research culture is experienced in community hospitals and offers targeted strategies for strengthening community hospital research culture at various levels of experience. Overall, strengthening community hospital research culture is necessary for improving Canadian health system research capacity.
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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 | 0.038 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.043 | 0.027 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".