The influence of community engagement on stakeholder perspectives in cross-sector integrated care: an integrative review
Bibliographic record
Abstract
Abstract Background Community engagement represents a highly relevant way to integrate care across sectors and address social and structural determinants of health with populations. Yet, advancement of integrated care remains a challenge, particularly across health and social service organizations. Situating social cognition as a key element of integrated care, this paper explores the act community engagement within cross-sector integrated care. Methods An integrative review was conducted to determine what is known about the influence of community engagement on stakeholder perspectives in cross-sector integrated care, and to contribute to a more comprehensive evidence base for building and operationalizing equitable integrated care. In March 2022, four data bases were systematically searched, applying no date limits, for English language articles that described community engagement in relation to integrated care and resulting stakeholder perspectives. Using matrices, numerous variables were extracted and synthesized using thematic analysis derived from the Rainbow Model of Integrated Care and a continuum of community engagement. Results In total, 13 studies were included in this analysis. Two studies included the hospital as a partner, and the rest were a mix of public, private health and social service sectors. Positive stakeholder perspectives (N = 6) were found in studies that were consultative or collaborative, and led with social capital, shared reciprocity, and trust. Moderate and negative perspectives (N = 7) were found in studies that led with a utilitarian stance and lacked collective leadership, governance, longitudinal planning, and joint evaluations. Conclusions This review makes a singular contribution to cross-sector integrated care literature, utilizing perspectives from health and social service organizations to map what is known about the influence of community engagement on cross-sector integrative care. Perspectives from this review support calls for additional integrative care research exploring community-hospital relationships, and how power dynamics influence proximal and distal relationships, capabilities, motivations, and opportunities for collaboration.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".