Mapping Tobacco Quitlines in North America: Signaling Pathways to Improve Treatment
Bibliographic record
Abstract
OBJECTIVES: This study was designed to better understand how the network of quitlines in the North American Quitline Consortium (NAQC) interact and share new knowledge on quitline practices. METHODS: Network relationship data were collected from all 63 publicly funded quitlines in North America, including information sharing, partner trust, and reputation. RESULTS: There was a strong tendency for US and Canadian quitlines to seek information from other quitlines in the same country, with few seeking information from quitlines from the other country. Quitlines with the highest reputation tended to more centrally located in the network, but the NAQC coordinating organization is highly central to the quitline network-thus demonstrating their role as a broker of quitline information. CONCLUSIONS: This first "snapshot" of US and Canadian quitlines demonstrated that smoking cessation quitlines in North America are not isolated, but are part of an interconnected network, with some organizations more central than others. As quitline use expands with the inclusion of national toll-free numbers on cigarette packs, how quitlines share information to improve practice will become increasingly important.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".