A qualitative examination of the implementation of a community–academic coalition
Bibliographic record
Abstract
Abstract Many recent grant initiatives have mandated coalition building as a key component of their health promotion efforts. Health service leaders are increasingly representing their organizations on coalitions and need to better understand the complexities involved in their creation and management. In this paper we focus on the implementation of a community–academic alliance using an ethnographic approach involving participant observation and in‐depth interviews. Analysis of the data revealed five essential dimensions to be considered in the development of successful community–academic alliances: membership, structure, leadership, communication, and funding. Within each of these areas, facilitators and barriers to successful coalition building were identified. While these areas are not new, obtaining the perspective of coalition members directly adds to our understanding of the member experience in the process of implementing such initiatives. This example of a community–academic collaboration presents one model of how to minimize the distance between research and service and move toward a partnership between these two worlds. © 2004 Wiley Periodicals, Inc. J Comm Psychol 32: 357–374, 2004.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".