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Record W3004700987 · doi:10.3390/ijerph17030992

Technology-Based Substance Use Interventions: Opportunities for Gender-Transformative Health Promotion

2020· review· en· W3004700987 on OpenAlexafffund
Julie Stinson, Lindsay Wolfson, Nancy Poole

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typereview
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsBritish Columbia Centre of Excellence for Women's Health
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionTransformative learningHealth promotionPromotion (chess)ConfidentialitySubstance useNarrativeEquity (law)PsychologyPublic relationsMedicinePolitical sciencePublic healthClinical psychologyNursingDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Drawing on data from a scoping review on sex, gender and substance use, this narrative review explores the use of gender-informed and technology-based approaches in substance use prevention and health promotion interventions. With an ever-changing landscape of new technological developments, an understanding of how technology-based interventions can address sex, gender, and intersecting equity considerations related to substance use is warranted. Current technology-based approaches to substance use prevention and health promotion are described and assessed for gender-specific and gender transformative outcomes, and limitations are discussed related to inclusivity, access, confidentiality, and a dearth of research on technological approaches that integrate gender-based analysis. A call for action designed to advance technology-based health promotion, prevention and brief interventions that address gender equity simultaneously with substance use is proposed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.713
GPT teacher head0.570
Teacher spread0.143 · 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.

Study designNot applicable
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

Citations14
Published2020
Admission routes2
Has abstractyes

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