Reducing decisional conflict in COVID-19 vaccination in ethnocultural communities through sensemaking: a participatory action mixed-methods study
Bibliographic record
Abstract
OBJECTIVE: To examine how cultural health brokers, as trusted intermediaries between formal systems and diverse ethnocultural communities, help navigate decisional conflict and misinformation regarding COVID-19 vaccination and to identify how their work contributes to system resilience in crisis contexts. DESIGN: A community-based participatory action sensemaking research project to capture the real-time work of cultural health brokers in helping people navigate decisional conflict for vaccination. SETTING, PARTICIPANTS: Multicultural Health Broker Cooperative in Edmonton, Alberta where brokers speak 54 languages and serve more than 10 000 people from diverse ethnolinguistic communities. 28 cultural health brokers (9 male; experience 4-25 years) contributed to data collection and analysis between 16 September 2021 and 16 December 2021. DATA COLLECTION AND ANALYSIS: The brokers captured real-time reflections and self-interpretations in the SenseMaker platform through a theoretically informed, codesigned, mixed-method data collection tool. The team engaged in 13 weekly, 90 minute, audio-recorded and transcribed sessions: seven focused on understanding and action planning and five reflecting on the SenseMaker data, the focus of the thematic analysis. Data were managed in NVivo (QSR International, Version 12, 2018). RESULTS: Brokers collected 277 narratives and conducted 13 sensemaking sessions. Understanding and purpose were identified in 68% of narratives as key to achieving coherence; 81% of narratives highlighted trust as crucial to what was needed for action; 62% of narratives reflected on a potential risk, with loss of trust a concern in 70% of them. A rich understanding of the sources of decisional conflict and misinformation was achieved and managed through outreach. There were four entwined components to navigation of the evolving complexity of COVID-19 vaccination: (1) building and sustaining trust; (2) strengthening relationships; (3) creating safe spaces for collective sensemaking and solution finding; and (4) leveraging cultural and social capital to address barriers. Through these mechanisms, brokers reduced decisional conflict and misinformation, supporting informed, values-congruent decisions. CONCLUSIONS: Cultural health brokers, embedded within communities and linked to formal systems, play a critical role in crisis response by fostering trust, mobilising resources and enabling collective sensemaking. This study demonstrates how these intermediaries' contextually and culturally attuned work provides a model for building system resilience for future crisis response.
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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 | 0.038 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".