A Matter of Identity: A Case Study Exploring the Promotion and Influence of Cross-Sector Integrated Care During COVID-19
Bibliographic record
Abstract
Cross-sector integrated care is increasingly seen as the route to improving health, advancing health equity, and reducing care fragmentation. While considerable literature has examined characteristics of successful integrated care initiatives, less is known about how sectoral, organizational, and professional boundaries may be overcome to support care unification. The field of health services research has much to learn from the rapid collaborative response that ensued during COVID-19. The purpose of this study was to explore and describe the impact of cross-sector integration utilized during COVID-19 at the individual and organizational level, through the Capability, Opportunity, and Motivation to change behaviour model. An exploratory case study was conducted with an inter-sectoral working group who engaged with and provided vaccines to a community of high COVID-19 incidence from April – September 2021 in Toronto, Canada. This study used three sources of data: key informant interviews (n= 10), key stakeholder interviews (n=4), organizational participants (n=2) and a review of relevant documents. Participants included front-line workers, managers, directors and executive directors from hospitals, community health centres, social care, government, and faith organizations. Data were inductively analyzed using Braun and Clark’s (2006) theoretical thematic analysis. Findings suggest that the success of this community centred initiative rested on the remarkable capability and collective efficacy of the inter-sectoral working group. Participants’ professional identity served as a key intrinsic motivator to support the achievement of normative integration during this rapid collaborative response. The fluid interplay of social processes known to facilitate cross-sector collaboration, namely distributive leadership, and informal organizing were central features to this initiative, where community knowledge was considered an essential resource by working group members and system leaders. While participants were proud of their accomplishments, many were disappointed with limited system learnings to advance integrated care, with communities, beyond COVID-19. Recommendations include a call for health system leaders to increasingly draw on opportunities for collective sectoral organizing grounded in complexity thinking, where sentinel focus areas are addressed through population health approaches. Through these collaborative acts, there is opportunity to bridge divides, drawing on internal motivations and collective governance to generate learning inclusive of community, addressing value for the system as a whole.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| 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".