Variation In Immunization Post-implementation Policy-making in Canada: A Comparative Case Study of Four Provinces
Bibliographic record
Abstract
Post-implementation immunization policy-making are complex evidence-informed deliberative processes aiming to optimize existing programs. In Canada, however, lack of explicitness, limited transparency, and substantial variation in vaccine decisions taken across jurisdictions have raised questions regarding the way these processes unfold and the rationale used to inform decisions. In this dissertation, I examined how and why immunization post-implementation policy-making varies across Canadian provinces with the aim of increasing knowledge on competing factors influencing these processes and their outputs, so that support could be targeted to enhance their transparency, timeliness, consistency, and rigor in the future. A comparative case study design was employed to examine these processes using the experience of four Canadian provinces (Alberta, British Columbia, Manitoba and Quebec) during two post-implementation decisions, selected as instrumental cases. Guided by a realist approach and a multi-level post-implementation immunization policy-making conceptual framework, I examined the role of structural, procedural, and contextual factors on the variation observed. For each province and decision, information from documents and interviews were analyzed using descriptive and interpretative approaches. Results from these cases were subsequently compared during a cross-case synthesis, allowing the identification of key insights and actionable lessons. Findings from this study underscore the complexity of immunization post-implementation policy-making in Canada, where variation across provinces appears to be multifactorial, but largely influenced by non-technical contextual factors (i.e., values, interests, and risk-tolerance of individuals and organizations involved in these processes). While it is important to continue ensuring that high-quality relevant scientific evidence is readily available for vaccine policy-making in a timely way, increasing attention should be paid to how decisions are made and to the role of individuals and organizations in influencing the way evidence is ultimately used to inform policy. Moving forward, immunization post-implementation deliberation processes should be transparent and include explicit discussion of the values, logic, assumptions, and trade-offs considered when examining the relative importance to be assigned to evidence, ethics, economics, and political considerations. This could enable more systematic/rigorous assessments and more nuanced discussions, potentially enhancing the overall policy-making process, and its outputs.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".