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 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 | 0.055 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".