The role of civil society organizations (CSOs) as community-based knowledge brokers: A qualitative study with CSOs during the first year of the COVID-19 pandemic in Canada
Bibliographic record
Abstract
Background: This study explored how civil society organizations in British Columbia, Canada, obtained, shared, and communicated multilingual COVID-19 information with people whose first language is not English. Aim: The aim was to examine civil society organizations’ role as community-based knowledge brokers during the first year of the COVID-19 pandemic. Methods: Commencing in December 2022, virtual semi-structured interviews were conducted with employees from civil society organizations in British Columbia (N=15). Results: Civil society organizations played a crucial role in sharing multilingual information with people whose first language is not English. They amplified public health messages, addressed confusion concerning public health orders, and engaged with community members to better understand and address local needs. Discussion: Civil society organizations contributed to health communication efforts and succeeded in reaching populations overlooked by mainstream communication channels. Conclusions: The COVID-19 pandemic provides an opportunity to reflect on the role of civil society organizations as community-based knowledge brokers that acted as intermediaries to support information-sharing from government public health communications to priority populations. Based on this study’s findings, we propose several recommendations to enhance equity-based preparedness, responses, and recovery for health emergencies.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".