Smoking Protective and Risk Factors Among Transgender and Gender-Expansive Individuals (Project SPRING): Qualitative Study Using Digital Photovoice
Bibliographic record
Abstract
BACKGROUND: Transgender and gender-expansive (TGE) adults are twice as likely to smoke cigarettes than cisgender individuals. There is a critical gap in research on effective and culturally sensitive approaches to reduce smoking prevalence among TGE adults. OBJECTIVE: This study aims to qualitatively examine the risk and protective factors of cigarette smoking among TGE adults through real-world exemplars. METHODS: We conducted a digital photovoice study among a purposeful sample of 47 TGE adults aged ≥18 years and currently smoking in the United States (March 2019-April 2020). Participants uploaded photos daily that depicted smoking risk and protective factors they experienced over 21 days on either private Facebook or Instagram groups. Next, we conducted separate focus group discussions to explore the experiences of these factors among a subset of participants from each group. We analyzed participants' photos, captions, and focus group transcripts and generated themes associated with smoking risk and protective factors. RESULTS: We identified 6 major themes of risk and protective factors of smoking among TGE individuals: experience of stress, gender affirmation, health consciousness, social influences, routine behaviors, and environmental cues. We describe and illustrate each theme using exemplar photos and quotes. CONCLUSIONS: The findings of this study will inform future community-engaged research to develop culturally tailored interventions to reduce smoking prevalence among TGE individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".