Public health preparedness in Alberta: a systems-level study
Bibliographic record
Abstract
BACKGROUND: Recent international and national events have brought critical attention to the Canadian public health system and how prepared the system is to respond to various types of contemporary public health threats. This article describes the study design and methods being used to conduct a systems-level analysis of public health preparedness in the province of Alberta, Canada. The project is being funded under the Health Research Fund, Alberta Heritage Foundation for Medical Research. METHODS/DESIGN: We use an embedded, multiple-case study design, integrating qualitative and quantitative methods to measure empirically the degree of inter-organizational coordination existing among public health agencies in Alberta, Canada. We situate our measures of inter-organizational network ties within a systems-level framework to assess the relative influence of inter-organizational ties, individual organizational attributes, and institutional environmental features on public health preparedness. The relative contribution of each component is examined for two potential public health threats: pandemic influenza and West Nile virus. DISCUSSION: The organizational dimensions of public health preparedness depend on a complex mix of individual organizational characteristics, inter-agency relationships, and institutional environmental factors. Our study is designed to discriminate among these different system components and assess the independent influence of each on the other, as well as the overall level of public health preparedness in Alberta. While all agree that competent organizations and functioning networks are important components of public health preparedness, this study is one of the first to use formal network analysis to study the role of inter-agency networks in the development of prepared public health systems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".