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Record W3009427464 · doi:10.1186/s12889-020-8152-8

Longitudinal impacts of an online safety and health intervention for women experiencing intimate partner violence: randomized controlled trial

2020· article· en· W3009427464 on OpenAlexafffundabout
Marilyn Ford‐Gilboe, Colleen Varcoe, Kelly Scott‐Storey, Nancy Perrin, Judith Wuest, C. Nadine Wathen, James Case, Nancy Glass

Bibliographic record

VenueBMC Public Health · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of New BrunswickUniversity of British ColumbiaWestern University
FundersCanadian Institutes of Health ResearchMcMaster UniversityUniversity of Missouri
KeywordsHelpfulnessMedicinePsychological interventionRandomized controlled trialPoison controlDomestic violenceSuicide preventionBiostatisticsIntervention (counseling)Clinical psychologyPublic healthInjury preventionOccupational safety and healthPsychiatryNursingPsychologyEnvironmental healthSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Responding to intimate partner violence (IPV) and its consequences is made complex by women's diverse needs, priorities and contexts. Tailored online IPV interventions that account for differences among women have potential to reduce barriers to support and improve key outcomes. METHODS: Double blind randomized controlled trial of 462 Canadian adult women who experienced recent IPV randomly were assigned to receive either a tailored, interactive online safety and health intervention (iCAN Plan 4 Safety) or a static, non-tailored version of this tool. Primary (depressive symptoms, PTSD symptoms) and secondary (helpfulness of safety actions, confidence in safety planning, mastery, social support, experiences of coercive control, and decisional conflict) outcomes were measured at baseline and 3, 6, and 12 months later via online surveys. Generalized Estimating Equations were used to test for differences in outcomes by study arm. Differential effects of the tailored intervention for 4 strata of women were examined using effect sizes. Exit survey process evaluation data were analyzed using descriptive statistics, t-tests and conventional content analysis. RESULTS: Women in both tailored and non-tailored groups improved over time on primary outcomes of depression (p < .001) and PTSD (p < .001) and on all secondary outcomes. Changes over time did not differ by study arm. Women in both groups reported high levels of benefit, safety and accessibility of the online interventions, with low risk of harm, although those completing the tailored intervention were more positive about fit and helpfulness. Importantly, the tailored intervention had greater positive effects for 4 groups of women, those: with children under 18 living at home; reporting more severe violence; living in medium-sized and large urban centers; and not living with a partner. CONCLUSION: This trial extends evidence about the effectiveness of online safety and health interventions for women experiencing IPV to Canadian women and provides a contextualized understanding about intervention processes and effects useful for future refinement and scale up. The differential effects of the tailored intervention found for specific subgroups support the importance of attending to diverse contexts and needs. iCAN is a promising intervention that can complement resources available to Canadian women experiencing IPV. TRIAL REGISTRATION: Clinicaltrials.gov ID NCT02258841 (Prospectively Registered on Oct 2, 2014).

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.108
GPT teacher head0.421
Teacher spread0.312 · 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 designRandomized 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

Citations80
Published2020
Admission routes3
Has abstractyes

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