MétaCan
Menu
Back to cohort
Record W2074554836 · doi:10.3109/09687630802378864

Recovered addicts working in the addiction field: Pitfalls to substance abuse relapse

2010· article· en· W2074554836 on OpenAlexaff
Nick Doukas, Jim Cullen

Bibliographic record

VenueDrugs Education Prevention and Policy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAddictionSubstance abuseEconomic shortageRelapse preventionPsychologyConstruct (python library)Addiction treatmentNarrativeField (mathematics)Qualitative researchPsychotherapistPsychiatrySociologyGovernment (linguistics)

Abstract

fetched live from OpenAlex

In the 1940s, due to a shortage of professional counselors, combined with the hope of rehabilitation for the addict, there grew a belief that the recovered alcoholic could be trained to enter the field of addiction treatment as a paraprofessional. These early stages of addiction treatment and the emergence of the recovered substance abuser as a counselor fostered a discussion in the role played by the paraprofessional. This discussion subsequently encouraged an accumulation of literature during the early stages of substance abuse treatment in North America, which later began to diminish as the field moved forward towards the twenty-first century. This paper reviews the literature to examine the perceived potential risks for relapse associated with recovered addicts working in the addictions field. Potential risks for relapse discussed are the ex-addict's motivation for entering the addiction field, personal help from self-help groups may be lost once in the field, over involvement with clients, over involvement with work, over identification with clients and the repercussions of relapse. The paper also addresses the limitations of the studies conducted to date, provides recommendations for further research and proposes that this topic be explored using a qualitative approach, so that recovered counselors can construct their own narratives.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.076
GPT teacher head0.433
Teacher spread0.357 · 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 designNot applicable
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

Citations34
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueDrugs Education Prevention and PolicySame topicMental Health and Patient InvolvementFrench-language works237,207