MétaCan
Menu
Back to cohort
Record W2102113453 · doi:10.1017/jsc.2013.34

R u a smkn m0m?: Aspects of a Text Messaging Smoking Cessation/Reduction Intervention for Younger Mothers

2013· article· en· W2102113453 on OpenAlexaff
Sophie Soklaridis, Jenna López, Karina Czyzewski, Rosa Dragonetti, Peter Selby

Bibliographic record

VenueThe Journal of Smoking Cessation · 2013
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsSmoking cessationIntervention (counseling)Psychological interventionPregnancyMedicinePostpartum periodFamily medicineFocus groupPsychologyNursing

Abstract

fetched live from OpenAlex

Introduction: Women who are younger in age are more likely to smoke during pregnancy and postpartum and tend to have less success with cessation/reduction. There is an unmet need for interventions targeted to pregnant and postpartum young women that provide them with support to quit/reduce long-term into the postpartum period and beyond. Aims: Our study aimed to gain an in-depth understanding of the perspectives of young pregnant and postpartum women of text messaging (TM) as a conduit for smoking cessation/reduction, and to determine the appropriate content, frequency, duration and unique features needed for an effective cessation/reduction TM programme. Methods: Six focus groups and six telephone interviews were convened with 36 pregnant and postpartum women 16–30 years of age. Results: Three main themes were identified: 1) topic areas that women would like TM to focus on; 2) the need for messages to be tailored; and 3) the importance for the programme to take a woman-centered approach. Conclusions: Respondents supported the idea of a TM cessation/reduction intervention and had clear programme recommendations. A personalised, woman-centered TM programme that meets a young woman's unique needs and addresses her concerns through her participation and direction is likely to empower and support her to quit/reduce.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.046
GPT teacher head0.390
Teacher spread0.344 · 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 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

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Smoking CessationSame topicMobile Health and mHealth ApplicationsFrench-language works237,207