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Record W2810023388 · doi:10.1093/bjsw/bcy051

Social Work’s Scope of Practice in Primary Mental Health Care: A Scoping Review

2018· review· en· W2810023388 on OpenAlexaff
Rachelle Ashcroft, Toula Kourgiantakis, Gwendolyn Fearing, Taylor Robertson, Judith Belle Brown

Bibliographic record

VenueThe British Journal of Social Work · 2018
Typereview
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsScope (computer science)Mental healthInclusion (mineral)Scope of practiceNursingSocial workHealth careWork (physics)Principal (computer security)MedicinePsychologyPublic relationsPolitical sciencePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

The inclusion of social workers as members of inter-professional primary health-care teams is an asset to health-care delivery by improving access to a broad range of psycho-social and mental health-care services and programmes. This scoping review examined the literature to summarise social work’s scope of practice in the provision of primary mental health care. Five electronic databases were searched within any given year to provide a comprehensive review of the literature. In the initial search, 4,800 articles were found and thirty met the inclusion criteria. One research team member reviewed the included articles independently with supervision from the principal investigator. Three categories emerged from the review: clinical responsibilities, social work activities in primary health-care and barriers to the implementation of social workers in such settings. An examination of social work’s scope of practice will help guide social work’s contribution to mental health care in inter-professional primary health care settings.

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)0.015
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0180.019
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.457
Teacher spread0.392 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations33
Published2018
Admission routes1
Has abstractyes

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