The Complexity of Institutionalizing Evaluation as a Best Practice in North American Quitlines
Bibliographic record
Abstract
Tobacco use continues to be the leading preventable cause of mortality and morbidity in North America and Quitlines are one of the primary cessation resources available to assist tobacco users with quitting. Implementation of best practices is important to the success of quitlines, but unfortunately, it is a complex and elusive process often difficult to achieve. This study aims to better understand the implementation process by using qualitative methods to examine an evaluation practice in-depth and to elucidate the complex factors influencing its implementation and institutionalization in the North American Quitline network. Nineteen semi-structured interviews were conducted with decision-makers in the Quitline network. The interview data were analyzed using a thematic analysis approach, guided by a systems change framework. The findings suggest that a broad range of factors influenced implementation of the evaluation practice at different levels of the system. These factors included system norms, system resources and operations (i.e., policies), five key relationships (e.g., between the funder and service provider), and power placement in the system. Characteristics of the evaluation practice itself also influenced implementation and interacted with other factors in the system. This study demonstrates the complexity of implementing and institutionalizing evaluation in an inter-organizational network. It also demonstrates the value of using qualitative data to study implementation phenomena. The findings can be used to improve efforts to institutionalize evaluation in the Quitline network and inform future implementation research studies.
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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 | 0.149 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 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".