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Record W4412784225 · doi:10.7202/1119013ar

The Impact of Neoliberalism on Social Justice-Based Social Work Practice in Mental Health

2025· article· en· W4412784225 on OpenAlexvenueaboutno aff
Catrina Brown, Donna Baines, Kaitrin Doll, Marjorie Johnstone

Bibliographic record

VenueCanadian social work review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsNeoliberalism (international relations)Social justiceMental healthSociologyWork (physics)Economic JusticeCriminologyPolitical sciencePsychologySocial sciencePsychiatryLawEngineering

Abstract

fetched live from OpenAlex

This article provides a critical view of the landscape of social workers’ experiences in mental health service provision in Canada. This national research project builds upon and supports the findings of a recent Nova Scotia study which found that the professional and social justice–based ethical paradigm of social work is often in conflict with the dominant biomedical model and is severely restricted by neoliberal managerial fiscal constraints on mental health service provision. Mental health social workers in Canada report a lack of professional autonomy, as well as professional disempowerment, devaluation, and a lack of decision-making opportunities in policy and approaches to practice. These results strongly suggest that, taken together, the climate of neoliberalism — with its emphasis on fiscal constraint and the rationalization of care and the corresponding biomedical, decontextualized, standardized, and individualized approach to mental healthcare — betrays social work’s social justice–based professional practice and identity, calling for the protection of the social work profession through establishing effective resistance.

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)0.027
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.141
Threshold uncertainty score0.996

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0180.091
Scholarly communication0.0150.005
Open science0.0030.013
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.444
Teacher spread0.393 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes2
Has abstractyes

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