Recognition: key to the entrepreneurial strategies of rural coalitions in advancing access to health care
Bibliographic record
Abstract
OBJECTIVES: Considerable evidence has advanced the role of citizen-led coalitions (CLC) in supporting the health and social needs of rural citizens. There has been little research focusing on the experiences and strategies of coalitions, with their limited resources and status, in targeting health inequities in their rural communities. The aim of this study was to understand the entrepreneurial strategies and experiences of rural coalitions to effect change in the delivery of health services for their older adult populations. METHOD: A qualitative descriptive study method was used to generate understanding of the entrepreneurial experiences and strategies of CLCs in advancing health services to meet the health and social needs of their citizens. Seven diverse CLCs (n = 40) from different rural communities participated in focus groups and in individual and coalition-level surveys. Thematic analysis was used to construct themes from the data. RESULTS: Two over-riding themes emerged: entrepreneurial strategies and societal recognition. CLCs engaged in numerous entrepreneurial strategies that enabled actions and outcomes in meeting their health care needs. These strategies included: securing quick wins, leveraging existing resources, and joining forces with stakeholder groups/individuals. However, despite these strategies and successes, coalitions expressed frustration with not being seen and not being heard by decision-makers. This pointed to a key structural barrier to coalition successes -- a broader societal and institutional problem of failing to recognize not only the health needs of rural citizens, but also the legitimacy of the community coalitions to represent and act on those needs. CONCLUSIONS: Despite the potential for coalitions to mobilize and effect change in addressing the inequities of rural health service access for older adults, broader barriers to their recognition, may undermine their entrepreneurial strategies and success.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".