A strategic initiative to foster interprofessional collaboration: A case report using a men’s healthy living programme
Bibliographic record
Abstract
Interprofessional collaboration (IPC) can be challenging, yet it has shown benefits for providers and patients. We examined the existing enablers and barriers to IPC at a local Community Health Center (CHC) and report on the existing types of IPC practice. We also report how implementing a men-sensitive healthy living programme united a team of health professionals/managers in attending to the needs of a population that to date had been largely underserved. A total of 16 employees were interviewed at the CHC. All respondents spoke positively of existing IPC and provided examples of existing collaborative practices. These CHC professionals (clinicians and managers) experienced greater intra- and extra-organisational collaborations as a result of experiencing a community-delivered programme. It also allowed the CHC professionals/managers to rethink the management and structure of collaborative practices and understand the needs and challenges of working with non-traditional partners (men and workplace-based managers). While our findings are context sensitive (case study design), they shed light on how uniting health professionals/managers around a challenging and non-traditional health issue (i.e., improving health in a hard-to-reach population subgroup) can strategically reduce resistance to collaborative practice development by strengthening team cohesion and fostering innovative interactions.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| 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 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".