The complexity of addressing equity in COVID-19-related global health governance and population health research priorities in Canada: a multilevel qualitative study
Bibliographic record
Abstract
BACKGROUND: Since COVID-19 emerged in 2020, the promotion of health equity, including in research, has further been challenged worldwide by both global health governance (GHG) processes and decisions, and national public health control measures. These global and national decisions have also led to the 'covidization' of health research agendas where resources have been massively channelled to address COVID-19, especially during the first years of the pandemic. This situation could potentially result in current and future population health research priorities not explicitly tackling equity as a central tenet. The study objective examined how and to what extent the COVID-19-related GHG architecture is affecting population health research priorities in Canada. METHODS: We conducted a multilevel qualitative study informed by the intersectionality-based policy analysis and multiple streams frameworks. We collected and thematically analysed data from four groups of respondents (n = 35: researchers, research funders and global and public health research institutes in Canada, and WHO/international actors) and an interactive feedback workshop (n = 40 participants). RESULTS: Study findings generated four main themes. First, both global and national COVID-19 responses failed to address equity considerations, especially among populations in situations of vulnerability and marginalisation. Second, the integrated examination of funding, equity, and accountability was judged as necessary determinants of GHG and population health research priorities in Canada. Third, contrary to common beliefs about COVID-19, the consequences were not all negative, but they were also positive and unintended, and lessons can be learned. Fourth, study respondents proposed multiple recommendations to address inequities in the complex intersection between COVID-19-related GHG and population health research in Canada. CONCLUSION: This study provides substantial evidence of the multilayered and complex intersection between COVID-19-related GHG and population health research priorities in Canada. Although the window of opportunity was slim according to study respondents, there was still a unique collective effort to address COVID-19-related socioeconomic and health inequities by considering the numerous recommendations proposed by the four groups of study respondents. These recommendations can directly contribute to improving knowledge of global and national population health and equity research strategies in the context of an evolving pandemic and for policy- and decision-makers to adjust and rectify the course of global and public health governance.
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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 | 0.032 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.035 | 0.021 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".