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Record W4412722072 · doi:10.1016/j.josat.2026.209983

“I Didn't Do it Alone”: Family Involvement in Recovery from Substance Use

2025· preprint· en· W4412722072 on OpenAlexfundno aff
Tanya Mudry, Christy Sander, Avery Sapoznikow, D. O’Brien, Shelbi Snodgrass

Bibliographic record

VenueJournal of Substance Use and Addiction Treatment · 2025
Typepreprint
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsSubstance usePsychologyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Family involvement has been identified as pivotal in addressing substance use disorder (SUD). However, family members, and the clinicians supporting them, are often uncertain about how best to help or be involved. The purpose of this study was to examine how those in recovery perceived family involvement in their recovery journey. METHODS: We conducted semi-structured interviews with individuals (n = 18) who identified as being in recovery from SUD. Interviews focused on specific practices that are perceived as helpful or hindering to recovery. The Enhanced Critical Incident Technique (ECIT) was used to analyze interview data and identify critical incidents. RESULTS: We identified 18 categories comprising of relational factors that helped and hindered recovery from SUD, organized into three main themes of factors important to individuals' recovery: interpersonal/relational factors, action, and knowledge and understanding. Participants provided concrete examples of how family members were helpful, hindering, or could have better supported their loved ones. CONCLUSIONS: Family support is critical in supporting recovery efforts of individuals struggling with substance use. Relational, action-oriented, and educational support should be collaboratively negotiated based on the preferences and unique needs of the individual in recovery.

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)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.000

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 head0.061
GPT teacher head0.284
Teacher spread0.223 · 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 designQualitative
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 abstractno

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