Coevolution of Information Sharing and Implementation of Evidence-Based Practices Among North American Tobacco Cessation Quitlines
Bibliographic record
Abstract
OBJECTIVES: We examined the coevolution of information sharing and implementation of evidence-based practices among US and Canadian tobacco cessation quitlines within the North American Quitline Consortium (NAQC). METHODS: Web-based surveys were used to collect data from key respondents representing each of 74 participating funders of NAQC quitlines during the summer and fall of 2009, 2010, and 2011. We used stochastic actor-based models to estimate changes in information sharing and practice implementation in the NAQC network. RESULTS: Funders were more likely to share information within their own country and with funders that contracted with the same service provider. Funders contracting with larger service providers shared less information but implemented significantly more practices. Funders connected to larger numbers of tobacco control researchers more often received information from other funders. Intensity of ties to the NAQC network administrative organization did not influence funders' decisions to share information or implement practices. CONCLUSIONS: Our findings show the importance of monitoring the NAQC network over time. We recommend increased cross-border information sharing and sharing of information between funders contracting with different and smaller service providers.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".