Are Black and Latino adolescents being asked if they use electronic cigarettes and advised not to use them? Results from a community-based survey
Bibliographic record
Abstract
Objective This study aimed to explore whether African American/Black and Hispanic/Latino adolescents are being asked about electronic cigarette (e-cigarette) use (vaping) and advised not to use them. Methods In 2021, adolescents (N = 362) with no vaping history, self-identified as African American/Black and/or Hispanic/Latino, and able to read and speak English and/or Spanish were recruited through partner schools and community-based organizations. Participants completed a survey reporting sociodemographic characteristics (e.g., race/ethnicity, gender, and language of preference) and they were asked about e-cigarette use and/or were advised not to use them by a health professional. Results In total, 12% of African American/Black and 5% of Hispanic/Latino participants reported not seeing a health professional in the year prior to enrollment. Of the participants who reported visiting a health professional, 50.8% reported being asked and advised about vaping. Over one-quarter (28.4%) of participants were neither asked nor advised regarding vaping. Compared to English-speaking participants, Spanish-speaking participants were significantly less likely to be asked about e-cigarette use (45.2 vs. 63.9%, p = 0.009) and advised not to use them (40.3 vs. 66.9%, p < 0.001). Moreover, compared to African American/Black participants, Hispanic/Latino participants were significantly less likely to be advised not to use e-cigarettes (52.9 vs. 68.6%, p = 0.018). Furthermore, compared to male participants, female participants were significantly less likely to be advised not to use e-cigarettes (51.3 vs. 68.2%, p = 0.003). Conclusion Compared to English-speaking participants, Spanish-speaking participants were significantly less likely to self-report being asked about e-cigarette use and advised not to use them. Moreover, Hispanic/Latino and female adolescents were significantly less likely to self-report being advised not to use e-cigarettes compared to their Black/African American and male counterparts. Future research is needed to improve health professional attention toward asking about and advising against vaping among adolescents.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".