A Motivational, Gender-Sensitive Smoking Cessation Resource for Family Members of Patients With Lung Cancer
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To gather feedback on an innovative gender-sensitive booklet that draws on emotional connections and relationship factors to motivate smoking cessation. RESEARCH APPROACH: Qualitative, descriptive. SETTING: Six provinces in Canada. PARTICIPANTS: 30 family members of patients with lung cancer who were currently smoking or had recently quit. METHODOLOGIC APPROACH: Parallel booklets for women and men were developed using language and images to emphasize family relationships and gender considerations to motivate smoking cessation. Participants were provided with the women's and men's versions of the resource, and they were asked to review the gender-specific version of the booklet that was relevant to them. Semistructured telephone interviews were conducted, and transcriptions were analyzed for themes. FINDINGS: Three themes were evident in the data, including "new perspectives. CONCLUSIONS: A gender-sensitive approach that focuses on relationship factors represents an acceptable way to engage relatives of patients with lung cancer in discussions to support smoking cessation. INTERPRETATION: Approaches to supporting smoking cessation among relatives of patients diagnosed with lung cancer should draw on positive relationship bonds and caring connections to motivate cessation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".