A longitudinal multi-site evaluation of community-based partnerships: implications for researchers, funders, and communities
Bibliographic record
Abstract
BACKGROUND: Innovative Models Promoting Access to Care Transformation (IMPACT) was a five-year (2013-2018), Canadian-Australian research program that aimed to use a community-based partnership approach to transform primary health care (PHC) organizational structures to improve access to appropriate care for vulnerable populations. Local Innovation Partnerships (LIPs) were developed to support the IMPACT research program, and to be ongoing structures that would continue to drive local improvements to PHC. METHODS: A longitudinal development-focused evaluation explored the overall approach to governance, relationships and processes of the LIPs in the IMPACT program. Semi-structured interviews were conducted with purposively selected participants including researchers with implementation roles and non-researchers who were members of LIPs at four time points: early in the development of the LIPs in 2014; during intervention development in 2015/2016; at the intervention implementation phase in 2017; and nearing completion of the research program in 2018. A hybrid deductive-inductive thematic analysis approach was used. A Guide developed to support the program was used as the framework for designing questions and analysing data using a qualitative descriptive method initially. A visual representation was developed and refined after each round of data collection to illustrate emerging themes around governance, processes and relationship building that were demonstrated by IMPACT LIPs. After all rounds of data collection, an overarching cross-case analysis of narrative summaries of each site was conducted. RESULTS: Common components of the LIPs identified across all rounds of data collection related to governance structures, stakeholder relationships, collaborative processes, and contextual barriers. LIPs were seen primarily as a structure to support implementation of a research project rather than an ongoing multisectoral community-based partnership. LIPs had relationships with many and varied stakeholders although not necessarily in ways that reflected the intended purpose. Collaboration was valued, but multiple barriers impeded the ability of LIPs to enact real collaboration in daily operations over time. We learned that experience, history, and time matter, especially with respect to community-oriented collaborative skills, structures, and relationships. CONCLUSIONS: This longitudinal multiple case study offers lessons and implications for researchers, funders, and potential stakeholders in community-based participatory research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".