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Record W2292936945 · doi:10.3390/ijerph13030282

Understanding Inequalities of Maternal Smoking—Bridging the Gap with Adapted Intervention Strategies

2016· review· en· W2292936945 on OpenAlexaff
Julie Boucher, Anne T. M. Konkle

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2016
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Ottawa
FundersAgency for Healthcare Research and Quality
KeywordsBridging (networking)InequalityPsychologyMedicineComputer scienceMathematicsComputer security

Abstract

fetched live from OpenAlex

Women who are generally part of socially disadvantaged and economically marginalized groups are especially susceptible to smoking during pregnancy but smoking rates are underreported in both research and interventions. While there is evidence to support the short-term efficacy of nicotine replacement therapy (NRT) use in pregnancy, long-term abstinence rates are modest. Current health strategies and interventions designed to diminish smoking in pregnancy have adopted a simplified approach to maternal smoking-one that suggests that they have a similar degree of choice to non-pregnant smokers regarding the avoidance of risk factors, and overlooks individual predictors of non-adherence. As a result, interventions have been ineffective among this high-risk group. For this reason, this paper addresses the multiple and interacting determinants that must be considered when developing and implementing effective strategies that lead to successful smoking cessation: socioeconomic status (SES), nicotine dependence, social support, culture, mental health, and health services. Based on our review of the literature, we conclude that tailoring cessation programs for pregnant smokers may ultimately optimize NRT efficacy and reduce the prevalence of maternal smoking.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.417
GPT teacher head0.474
Teacher spread0.057 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations40
Published2016
Admission routes1
Has abstractyes

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