The identification of framed messages in the New York State Smokers' Quitline materials
Bibliographic record
Abstract
Research suggests that smoking cessation messages are most persuasive when framed in terms of the benefits achieved from quitting (i.e. gain-framed) than when framed in terms of the costs of not quitting (i.e. loss-framed). It is unknown, however, if these findings about optimal message frames have been translated into public health practice. The current study examined message framing in telephone counseling sessions with smokers calling the New York State Smokers' Quitline (NYSSQ). We conducted a content analysis of all NYSSQ print material and 12 Quitline service calls. Two independent raters coded each message within these documents as being gain-framed, loss-framed or non-framed. Messages from the service calls also were coded for their function (e.g. information provision, information gathering). Interrater reliability was acceptable (kappa > 0.80). Of the 997 print messages evaluated, 21.6% were gain-framed, 13.8% were loss-framed and 64.6% were non-framed. For the service calls, only the messages with an information provision function included framed content. Of the 420 information provision messages, 10.2% were gain-framed, 1.7% were loss-framed and 88.1% were non-framed. The loss-framed and non-framed messages indicate missed opportunities for providing gain-framed messages within the Quitline services, thus emphasizing a possible gap between research and practice.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".