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Record W4385718213 · doi:10.3389/fpubh.2023.1222184

Are Black and Latino adolescents being asked if they use electronic cigarettes and advised not to use them? Results from a community-based survey

2023· article· en· W4385718213 on OpenAlexaboutno aff
Margaret Connolly, Daniel P. Croft, Paula Ramírez‐Palacios, Xueya Cai, Beverly Hill, Rafael H. Orfin, M. Patricia Rivera, Karen M. Wilson, Dongmei Li, Scott McIntosh, Deborah J. Ossip, Ana Paula Cupertino, Francisco Cartujano‐Barrera

Bibliographic record

VenueFrontiers in Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineEthnic groupAfrican americanElectronic cigaretteFamily medicineQuarter (Canadian coin)Community healthDemographyPublic healthGerontologyNursing

Abstract

fetched live from OpenAlex

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.

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

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.121
GPT teacher head0.331
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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