Minimal dataset for quitlines: a best practice
Bibliographic record
Abstract
OBJECTIVES: This paper discusses the development of a minimal dataset (MDS) for tobacco cessation quitlines across North America. The goal was to create a standardised instrument and protocol that would allow for comparisons and pooling of data across quitlines for evaluation and research purposes. Principles of utilisation focused evaluation were followed to achieve consensus across diverse stakeholder groups in two countries. METHODS: The North American Quitline Consortium (NAQC) assembled a working group with representatives from quitline service providers, funders, evaluators and researchers from Canada and the United States. An extensive, iterative consultation process over two years led to consensus on the evaluation domains, indicators and specific items. Descriptive information on quitline service models, data collection protocols and methodological issues were addressed. RESULTS: The resulting minimal dataset (MDS) includes 15 items collected from eligible callers at intake and eight items collected from smokers participating in evaluation. Recommendations for selecting evaluation participants, length of follow-up and repeat callers were developed. Full MDS questions and technical documents are available on the NAQC website. CONCLUSION: Adoption and implementation of the MDS occurred in the majority of North American quitlines by the end of 2006. Key success factors included a focus on utility and feasibility, a commitment to meeting multiple and varied needs, sensitivity to situational factors and investment in working interactively with stakeholders. The creation and implementation of a MDS across two countries is an important "first" in tobacco control which will help speed the creation of practice based evidence and facilitate practice based research.
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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 | 0.163 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".