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Record W2912842940 · doi:10.1093/bjsw/bcy104

Challenging Social Work Epistemology in Children’s Mental Health: The Connections Between Evidence-Based Practice and Young People’s Psychiatrisation

2018· article· en· W2912842940 on OpenAlexaff
Maria Liegghio, David Delay, Angélique Jenney

Bibliographic record

VenueThe British Journal of Social Work · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of CalgaryAlberta Children's HospitalUniversity of ManitobaYork University
Fundersnot available
KeywordsMental healthSocial workStigma (botany)Evidence-based practiceDistressPsychologyMental illnessMental distressSociologySocial psychologyPsychotherapistMedicinePsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

Abstract Broadly, social work epistemology—what we know and how we know what we know—in child and adolescent mental health is dominated by professional discourses about the merits of evidence-based practice (EBP). As an approach to client-work and professional decision making, EBP is promoted as ‘the’ standard for an effective, efficient and ethical practice. Yet, as a discipline, have we failed to consider the ways our adoption of EBP may inadvertently contribute to potentially harmful social constructions of young people and their experiences of distress? Particular to mental health work, have we failed to consider the ways EBP may contribute to young people’s psychiatrisation and the stigma of mental illness? Towards answering these questions, the purpose of this paper is to explore social work’s epistemology and adoption of EBP in children’s mental health against the backdrop of psychiatrisation and stigma. In two parts, first, we review the principles of EBP and the application to children’s mental health. Second, we examine the particular social constructions to emerge about young people and their experiences of distress and how they relate to psychiatrisation and stigma. We conclude with a call for social justice research to inform social work practice.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0180.001
Scholarly communication0.0010.001
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 head0.041
GPT teacher head0.365
Teacher spread0.324 · 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.

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

Citations9
Published2018
Admission routes1
Has abstractyes

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