Everybody's business: economic surveillance of public health services in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: To address public health risk factors, governments conduct interventions in many different ministries, including non-health ministries. In order to understand the scope and cost of public health in Alberta, we developed a survey of government public health interventions. We included any government ministry or public organization, which includes health as a stated objective. METHODS: A grey literature search was initially conducted, followed by 69 consultations with federal, provincial and municipal organizations. We captured information related to (i) the type of public health service provided; (ii) the associated costs (if available); and (iii) any additional ministry that may collaborate on the initiative. This information was then presented to lead ministry personnel for validation and verification. RESULTS: We covered 15 areas of public health and identified 23 federal and 21 provincial agencies and departments that were providing these services. Public health spending on current operations amounted to $327 per capita, of which 60.5% came from provincial non-health ministries. Capital expenditures were $256 per capita, of which 32.5% were from the federal government. CONCLUSIONS: Public health expenses by non-health ministries were greater than those for health ministries. Capital expenses were much greater than non-capital expenses. In order to measure the full impact of government public health, it is necessary to take a cross-ministerial approach.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".