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Record W2888249283 · doi:10.1080/07347324.2018.1502642

Life in Recovery from Addiction in Canada: Examining Gender Pathways with a Focus on the Female Experience

2018· article· en· W2888249283 on OpenAlexafffundabout
Robyn J. McQuaid, Colleen Anne Dell

Bibliographic record

VenueAlcoholism Treatment Quarterly · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of SaskatchewanCanadian Centre on Substance Use and AddictionUniversity of OttawaRoyal Ottawa Mental Health Centre
FundersHealth Canada
KeywordsAddictionPsychologyFocus (optics)Psychiatry

Abstract

fetched live from OpenAlex

The first Life in Recovery (LIR) from Addiction survey in Canada showed that individuals experience various pathways to recovery. The current study examines the recovery experiences among adult females with data from the 2016 Canadian LIR survey, with a focus on two well-established predictors of female substance use disorders, namely, family violence and mental health, for their impact on the recovery journey. Reasons for starting recovery varied by females and males, as a higher proportion of females (70.2%) reported their mental health as a factor for wanting to start recovery compared to males (64.7%). Moreover, females reported greater untreated mental health or emotional concerns during addiction compared to males, p = .003, as well as greater family violence during recovery, p = .02 and addiction, p < .001. In terms of informal supports, females are more likely to use technology as a recovery support, p < .05, are more likely to connect with a pet or other animal, p = .001, and are more likely to use art, poetry, writing, p = .007, and yoga, p < .001, as part of their recovery journey compared to males. These findings reveal females’ unique addiction and recovery experiences and highlight the importance of considering gender implications, particularly for mental health and family violence, during the journey of addiction and 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 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 score0.391
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.068
GPT teacher head0.257
Teacher spread0.189 · 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

Citations14
Published2018
Admission routes3
Has abstractyes

Explore more

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