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Record W4410571954 · doi:10.2196/72425

A Web-Based Well-Being and Resilience Intervention for Family Members and Friends Supporting a Loved One Using Alcohol and Other Drugs: Mixed Methods Pilot Study

2025· article· en· W4410571954 on OpenAlexvenueno aff
Stephanie Kershaw, Jessica Deng, Madeleine Keaveny, Bronte Speirs, Anna Grager, Dara Sampson, Kate Ross, Nicola C. Newton, Maree Teesson, Frances Kay‐Lambkin, Cath Chapman

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintPsychologyIntervention (counseling)Resilience (materials science)PsychotherapistAlcohol use disorderPsychological resilienceAlcoholClinical psychologyPsychiatryWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Background: Despite the known psychosocial challenges associated with supporting a loved one using alcohol and other drugs (AOD), there is a scarcity of mental health and well-being interventions for affected friends and family members (AFFMs). Stigma has also been shown to discourage help-seeking among AFFMs. Web-based interventions may facilitate help-seeking by ensuring privacy and anonymity. Objective: This pilot study examines the usability, acceptability, and feasibility of the Family and Friend Support Program (FFSP), a world-first, evidence-based web-based resilience and well-being program designed with, and for, people caring for someone using AOD. This study also examined AFFM's experiences of caring for a loved one using AOD and their help-seeking behaviors and barriers. Methods: In 2021 (November-December), participants across Australia completed a baseline web-based cross-sectional survey that assessed the impact of caring for a loved one using AOD (adapted Short Questionnaire for Family Members-Affected by Addiction), and distress levels (Kessler Psychological Distress Scale [K-10]). Following baseline, participants were invited to interact with the FFSP over 10 weeks. Postprogram and follow-up surveys (10 and 14 wk postbaseline, respectively) and semistructured interviews assessed the usability and acceptability of the program, as well as help-seeking experiences and barriers. Results: Baseline surveys were completed by 131 AFFMs, with 37% (n=49) completing the postprogram survey and 24% (n=32) completing the follow-up survey. A total of 5 participants took part in individual semistructured interviews at postprogram. On average, K-10 scores fell in the moderate to severe range at baseline (mean 28.4, SD 8.6). At postprogram, the majority of participants (n=27, 55.1%) reported that they did not seek help to cope with or manage their role supporting their loved one and the most common endorsed barrier was cost (n=11, 28.6%). Overall, participants found the FFSP easy to use and provided them with relevant, helpful, and validating information. The majority (n=35, 71.5%) of participants said they would be likely to recommend the FFSP to a person supporting a loved one using AOD. Qualitative responses highlighted the need for free, accessible support for AFFMs such as the FFSP. Limitations included low program engagement and high attrition. Conclusions: Overall, the FFSP appears to be a promising mental health intervention for AFFMs. This study builds on existing research finding high levels of distress among AFFMs, while highlighting the ongoing barriers to help-seeking. Limitations and future directions for refinements and future efficacy evaluation of the FFSP are discussed including ways to address attrition and increase 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)0.004
metaresearch head score (Gemma)0.004
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: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.094
GPT teacher head0.502
Teacher spread0.408 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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