Enhancing capacity for risk factor surveillance at the regional/local level: a follow-up review of the findings of the Canadian Think Tank Forum after 4 years
Bibliographic record
Abstract
BACKGROUND: National health surveys are sometimes used to provide estimates on risk factors for policy and program development at the regional/local level. However, as regional/local needs may differ from national ones, an important question is how to also enhance capacity for risk factor surveillance regionally/locally. METHODS: A Think Tank Forum was convened in Canada to discuss the needs, characteristics, coordination, tools and next steps to build capacity for regional/local risk factor surveillance. A series of follow up activities to review the relevant issues pertaining to needs, characteristics and capacity of risk factor surveillance were conducted. RESULTS: Results confirmed the need for a regional/local risk factor surveillance system that is flexible, timely, of good quality, having a communication plan, and responsive to local needs. It is important to conduct an environmental scan and a gap analysis, to develop a common vision, to build central and local coordination and leadership, to build on existing tools and resources, and to use innovation. CONCLUSIONS: Findings of the Think Tank Forum are important for building surveillance capacity at the local/county level, both in Canada and globally. This paper provides a follow-up review of the findings based on progress over the last 4 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".