Investigating the influence of institutions, politics, organizations, and governance on the COVID-19 response in British Columbia, Canada: A jurisdictional case study protocol
Bibliographic record
Abstract
Abstract Background:Research on public health responses to COVID-19 globally has largely focused on understanding the virus’ epidemiology, identifying interventions to curb transmission, and assessing the impact of interventions on outcomes. Only recently have studies begun to situate their findings within the institutional, political, or organizational contexts of jurisdictions. Within British Columbia (BC), Canada, the COVID-19 response in early 2020 was deemed highly coordinated and effective overall; however, little is understood as to how these ‘upstream’ factors influenced policy decisions.Methods:Using a conceptual framework we developed, we are conducting a multidisciplinary jurisdictional case study to explore the influence of institutional (I), political (P), organizational (O), and governance (G) factors on BC’s COVID-19 public health response in 2020-2021. A document review (e.g. policy documents, media reports) is being used to: (1) characterize relevant institutional and political factors in BC; (2) identify key policy decisions in BC’s epidemic progression; (3) create an organizational map of BC’s public health system structure; and (4) identify key informants for interviews. Quantitative data (e.g. COVID-19 case, hospitalization, death counts) from publicly accessible sources will be used to construct BC’s epidemic curve. Key informant interviews (n = 15-20) will explore governance processes in the COVID-19 response and triangulate data from prior procedures. Qualitative data will be analyzed using a hybrid deductive-inductive coding approach and framework analysis. By integrating all of the data streams, our aim is to explore decision-making processes, identify how IPOG factors influenced policy decisions, and underscore implications for decision-making in public health crises in the BC context, and elsewhere. Knowledge users within the jurisdiction will be consulted to construct recommendations for future planning and preparedness.Discussion:As the COVID-19 pandemic evolves, governments have initiated ‘post-mortem’ examinations of their policies to identify lessons learned. Our conceptual framework articulates how interrelations between IPOG contextual factors might be applied to such analysis. Through this jurisdictional case study, we aim to contribute findings to strengthen governmental responses and improve preparedness for future health crises. This protocol can be adapted to and applied in other jurisdictions, across sub-national jurisdictions, and internationally.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".