Public Health Financing in British Columbia: A Case Study Investigating Factors Influencing Decision-Making
Bibliographic record
Abstract
To address the gap in research on public health financing in Canada, this study aimed to 1) describe public health budget-setting processes in British Columbia (BC), including roles of stakeholders involved, and 2) identify and analyse the factors influencing decision-making for public health resource allocation. My case study of the BC public health system consisted of a jurisdictional review of academic and grey literature on BC financing processes and trends spanning two decades, and semi-structured interviews with 14 key informants influential in budget-setting for public health. Taking an inductive analytical approach, I constructed a conceptual model of the political, structural, and external factors influencing public health financing decisions based on results from the case study. I conclude by identifying policy directions with potential for promoting stable public health funding, such as including public health experts in financial decision-making, and strengthening partnerships with external public health organizations. Pour répondre au manque de recherche sur le financement de la santé publique au Canada, cette étude a visé à 1) décrire les processus budgétaires de santé publique en Colombie Britannique (CB) en incluant les rôles des parties prenantes, et 2) identifier et analyser les facteurs influant la prise de décisions pour l’allocation de ressources à la santé publique. Mon étude de cas du système de santé publique de la CB a consisté en une revue juridictionnelle des littératures académique et grise sur les processus et tendances de financement en CB au cours des deux dernières décennies, et de 14 entrevues semi-structurées avec des participants clés influant l’élaboration du budget de santé publique. En prenant une approche analytique inductive, j’ai construit un modèle conceptuel des facteurs politiques, structurels, et externes influant les décisions de financement de la santé publique basé sur mes résultats de l’étude de cas. Je conclus en identifiant des options politiques avec le potentiel de promouvoir un financement de santé publique stable, telle qu’inclure les experts en santé publique dans la prise de décisions, et de renforcer les partenariats avec les organisations de santé publique externes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".