A Gender-Informed Approach for Smoking Cessation Support in Women with Diabetes
Bibliographic record
Abstract
Context Women with diabetes mellitus (DM) who smoke are at elevated risk of stroke and acute coronary events, yet few smoking cessation interventions are tailored to their unique needs. Sex (biological) and gender (social) related factors contribute to disparities in smoking harms and lower quit rates among women. Existing cessation programs often neglect sex and gender differences. A gender-informed approach may improve quit outcomes and reduce health inequities among women with DM. Objective To synthesize evidence on smoking cessation interventions that address sex and gender-related influences on smoking in women, and engage stakeholders to co-develop knowledge mobilization (KM) tools tailored for women with DM. Study Design and Analysis This study was conducted in three phases: (1) a systematic review of women-specific smoking cessation programs published since 2010, including analysis of strategies used to address sex and gender-related barriers; (2) stakeholder consultation workshops with patients, researchers, and clinicians to adapt review findings for women with DM; and (3) co-design of KM products including written materials, a podcast, and an animated video. Setting The study was conducted at the INTREPID Lab at the Centre for Addiction and Mental Health (CAMH) with national collaborators from Diabetes Action Canada and the Centre of Excellence for Women’s Health. Population Studied Women who smoke, with a particular focus on women with DM. Intervention No direct intervention was delivered to patients; instead, the research team and stakeholders co-created KM tools tailored for use by patients, healthcare providers, and health organizations. Outcome Measures This work included a systematic review of the literature of women-only smoking cessation programs. Using the evidence gathered in the literature review, our participatory approach yielded three KM products that aim to help women with DM quit smoking. These included: animation video, written materials for patients, and a podcast. Results We have created a patient-facing animation video (https://youtu.be/ge6Tq-Kmya0), written materials, and a podcast episode that will be disseminated widely through Diabetes Action Canada. Conclusions Through stakeholder collaboration and co-designed KM tools, this project has the potential to improve quit outcomes and overall health in this group.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".