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Record W2962648445 · doi:10.1002/capr.12247

Social justice competencies for counselling and psychotherapy: Perceptions of experienced practitioners and implications for contemporary practice

2019· article· en· W2962648445 on OpenAlexafffund
Jason Brown, Samantha Wiendels, Vanessa Eyre

Bibliographic record

VenueCounselling and Psychotherapy Research · 2019
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPerspective (graphical)Social workCritical consciousnessSocial justicePerceptionSocial consciousnessEconomic JusticePsychotherapistMedical educationPedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

Abstract The purpose of the present study was to identify social justice competencies from the perspective of psychotherapists engaged in therapeutic practice. Twenty‐five therapists were asked, “What social justice competencies do psychotherapists need?” Responses were analysed using the concept mapping method. Nine participants grouped all unique interview responses into groups. Multidimensional scaling and cluster analysis were applied. The six competency areas identified included community activism, political influence on clinical work, critical consciousness, social responsibility, self‐awareness and personal style. The results were compared and contrasted with the literature. Considerable overlap was noted. The main differences concerned the need for collaboration as an advocacy tactic, as well as local knowledge about the pressing social issues affecting members of the community within which one practices.

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)0.017
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.007
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0020.003
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 head0.153
GPT teacher head0.488
Teacher spread0.335 · 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 designQualitative
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

Citations10
Published2019
Admission routes2
Has abstractyes

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