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Record W3191930452 · doi:10.1080/02650533.2021.1964454

Social work in addiction: opportunities and alliances

2021· article· en· W3191930452 on OpenAlexaff
Abreham Mekonnen, Bonnie K. Lee

Bibliographic record

VenueJournal of Social Work Practice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsAddictionSocial workPsychologyCompetence (human resources)Intervention (counseling)Clinical social workMental healthSocial competenceAction (physics)Addiction medicineSociologySocial psychologyPsychotherapistSocial changePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Addiction is a severe social and mental health problem that requires concerted interdisciplinary efforts to address. Social workers routinely encounter addiction in their practice and can play an important role in its prevention, intervention and policies. However, social workers’ competence in working with addiction is currently compromised by inadequate academic training and reliance on a dominant medical and individualistic paradigm that is at odds with the person-environment philosophy of social work. Based on a critical review of the literature, three addiction theories that feature ecological, relational and humanistic-systemic concepts and practice compatible with social work orientation are presented. Existing knowledge gaps in addiction, formation of interdisciplinary allies and action steps are identified for social work to contribute to addiction theory, research and practice more centrally.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0070.018
Scholarly communication0.0100.012
Open science0.0010.011
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.105
GPT teacher head0.405
Teacher spread0.300 · 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 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

Citations8
Published2021
Admission routes1
Has abstractyes

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