Understanding collaborative implementation between community and academic partners in a complex intervention: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: Community-academic partnerships (CAPs) can improve the relevance, sustainability, and uptake of new innovations within the community. However, little is known about what topics CAPs focus on and how their discussions and decisions impact implementation at ground level. The objectives of this study were to better understand the activities and learnings from implementation of a complex health intervention by a CAP at the planner/decision-maker level, and how that compared to experiences implementing the program at local sites. METHODS: The intervention, Health TAPESTRY, was implemented by a nine-partner CAP including academic, charitable organizations, and primary care practices. Meeting minutes were analyzed using qualitative description, latent content analysis, and a member check with key implementors. An open-answer survey about the best and worst elements of the program was completed by clients and health care providers and analyzed using thematic analysis. RESULTS: In total, 128 meeting minutes were analyzed, 278 providers and clients completed the survey, and six people participated in the member check. Prominent topics of discussion categories from the meeting minutes were: primary care sites, volunteer coordination, volunteer experience, internal and external connections, and sustainability and scalability. Clients liked that they learned new things and gained awareness of community programs, but did not like the volunteer visit length. Clinicians liked the regular interprofessional team meetings but found the program time-consuming. CONCLUSIONS: An important learning was about who had "voice" at the planner/decision-maker level: many of the topics discussed in meeting minutes were not identified as issues or lasting impacts by clients or providers; this may be due to differing roles and needs, but may also identify a gap. Overall, we identified three phases that could serve as a guide for other CAPs: Phase (1) recruitment, financial support, and data ownership; Phase (2) considerations for modifications and adaptations; Phase (3) active input and reflection.
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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 | 0.039 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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".