MétaCan
Menu
Back to cohort

Identity in recovery from problematic alcohol use: A qualitative study of online mutual aid

2017· article· en· W2594798061 on OpenAlexfundno aff
Sophia E. Chambers, Krysia Canvin, David S. Baldwin, Julia Sinclair

Bibliographic record

VenueDrug and Alcohol Dependence · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersMcGill University
KeywordsSobrietyIdentity (music)SecrecyPsychologyMutual aidSocial psychologyAlcoholics AnonymousQualitative researchInternet privacyComputer securityComputer scienceSociologyClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

AIM: To explore how engagement with online mutual aid facilitates recovery from problematic alcohol use, focusing on identity construction processes. DESIGN: Qualitative in-depth interview study of a maximum variation sample. SETTING: Telephone interviews with UK-based users of Soberistas, an online mutual aid group for people who are trying to resolve their problematic alcohol use. PARTICIPANTS: Thirty-one members, ex-members and browsers of Soberistas (25 women, 6 men): seven currently drinking, the remainder with varying lengths of sobriety (two weeks to five years). FINDINGS: Three key stages of engagement were identified: 1) 'Lurking' tended to occur early in participants' recovery journeys, where they were keen to maintain a degree of secrecy about their problematic alcohol use, but desired support from likeminded people. 2) Actively 'participating' on the site and creating accountability with other members often reflected an offline commitment to make changes in drinking behaviour. 3) 'Leading' was typically reserved for those securely alcohol-free and demonstrated a long-standing commitment to Soberistas; leaders described a sense of duty to give back to newer members in early recovery and many reported an authentic identity, defined by honesty, both on- and off-line. CONCLUSIONS: Engagement with online mutual aid might support recovery by affording users the opportunity to construct and adjust their identities in relation to their problematic alcohol use; individuals can use the parameters of being online to protect their identity, but also as a mechanism to change and consolidate their offline alcohol-related identity.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.087
GPT teacher head0.399
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 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

Citations29
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueDrug and Alcohol DependenceSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207