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Record W3082818095 · doi:10.18192/aporia.v12i1.4841

How Companion Animals Support Recovery from Opioid Use Disorder: An Exploratory Study of Patients in a Methadone Maintenance Treatment Program

2020· article· en· W3082818095 on OpenAlexvenueaboutno aff
Brynn Kosteniuk, Colleen Anne Dell

Bibliographic record

VenueAporia · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentThematic analysisMental healthSubstance abuseMethadone maintenancePsychologyOpioid use disorderMethadoneSocial supportSubstance useVulnerability (computing)PsychiatryMedicinePsychotherapistQualitative researchOpioidPolitical scienceSociology

Abstract

fetched live from OpenAlex

The past decade has witnessed increased attention to the benefits of companion animals on human health, though little attention has been paid to the potential to support recovery from a substance use disorder. Amidst an opioid crisis in Canada, studying this overlooked source of support may be beneficial. This study explores how companion animals support the recovery of seven methadone maintenance treatment patients in a Canadian core neighborhood. Through semi-structured interviews and a thematic analysis, the findings demonstrate that individuals’ companion animals support their recovery in four areas of their lives: (i) social, (ii) health and wellbeing, (iii) home, and (iv) purpose and empowerment. These themes were found to align with and expand upon the four dimensions of a Life in Recovery outlined by the Substance Abuse and Mental Health Services Administration. Structuring the paper by the expanded categories, this study introduces how companion animals fulfilled supportive roles that other humans could not or chose not to provide, while the human-animal bond encouraged a strengths-based approach to individuals’ recovery. This helped foster positive self-identity and a perceived choice over individuals’ recovery pathways.

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 score0.100
Threshold uncertainty score0.638

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 head0.044
GPT teacher head0.327
Teacher spread0.282 · 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

Citations12
Published2020
Admission routes2
Has abstractyes

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