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Record W4388050943 · doi:10.2196/49718

Web-Based Delivery of a Family-Based Dating Abuse Prevention Program for Adolescents Exposed to Interparental Violence: Feasibility and Acceptability Study

2023· article· en· W4388050943 on OpenAlexvenueno aff
H. Luz McNaughton Reyes, Eliana G Armora Langoni, Laurel Sharpless, Kathryn E. Moracco, Quetzabel Benavides, Vangie A. Foshee

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
FundersNational Center for Injury Prevention and ControlUniversity of North Carolina at Chapel HillCenters for Disease Control and Prevention
KeywordsDomestic violencePsychologySuicide preventionDating violencePoison controlWorksheetProgram evaluationMedicineClinical psychologyMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Numerous studies have demonstrated that exposure to caregiver intimate partner violence (IPV) can have cascading negative impacts on children that elevate the risk of involvement in dating abuse. This cascade may be prevented by programs that support the development of healthy relationships in children exposed to IPV. This paper describes the results of a study of the web-based adaptation of an evidence-based dating abuse prevention program for IPV-exposed youth and their maternal caregivers. Core information and activities from an evidence-based program, Moms and Teens for Safe Dates, were adapted to create the web-based program (e-MTSD), which comprises 1 module for mothers only and 5 modules for mother-adolescent dyads to complete together. OBJECTIVE: The primary objective of this study was to evaluate the feasibility and acceptability of the e-MTSD program and the associated research processes. We also examined the practicability of randomizing mothers to receive SMS text message reminders and an action planning worksheet, which were intended to support engagement in the program. METHODS: Mothers were recruited through community organizations and social media advertising and were eligible to participate if they had at least one adolescent aged 12 to 16 years of any gender identity who was willing to participate in the program with them, had experienced IPV after their adolescent was born, and were not currently living with an abusive partner. All mothers were asked to complete the program with their adolescent over a 6- to 8-week period. Participants were randomized to receive SMS text message reminders, action planning, or both using a 2×2 factorial design. Research feasibility was assessed by tracking recruitment, randomization, enrollment, and attrition rates. Program feasibility was assessed by tracking program uptake, completion, duration, and technical problems, and acceptability was assessed using web-based surveys. RESULTS: Over a 6-month recruitment period, 101 eligible mother-adolescent dyads were enrolled in the study and were eligible for follow-up. The median age of the adolescent participants was 14 years; 57.4% (58/101) identified as female, 32.7% (33/101) identified as male, and 9.9% (10/101) identified as gender diverse. All but one mother accessed the program website at least once; 87.1% (88/101) completed at least one mother-adolescent program module, and 74.3% (75/101) completed all 6 program modules. Both mothers and adolescents found the program to be highly acceptable; across all program modules, over 90% of mothers and over 80% of adolescents reported that the modules kept their attention, were enjoyable, were easy to do, and provided useful information. CONCLUSIONS: Findings suggest the feasibility of web-based delivery and evaluation of the e-MTSD program. Furthermore, average ratings of program acceptability were high. Future research is needed to assess program efficacy and identify the predictors and outcomes of program engagement.

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 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)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.149
GPT teacher head0.499
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations2
Published2023
Admission routes1
Has abstractyes

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