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Record W4402646892 · doi:10.1080/10640266.2024.2405291

Building the representation of male mental health professionals in eating disorder treatment

2024· article· en· W4402646892 on OpenAlexaff
Kyle T. Ganson, Douglas W. Bunnell

Bibliographic record

VenueEating Disorders · 2024
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental healthPsychologyPsychiatryEating disordersRepresentation (politics)Health professionalsPsychotherapistClinical psychologyHealth carePoliticsPolitical science

Abstract

fetched live from OpenAlex

Male mental health professionals (e.g. social workers, psychologists) are a minority of providers in eating disorder treatment spaces, and there is a drastic need to increase their representation in this clinical area. This Last Word outlines the barriers that impede male mental health professionals from specializing in eating disorder treatment, such as masculine gender norms, and provides four specific recommendations to enhance training, hiring, retention, and the development of male mental health professionals in the treatment of people with eating disorders. These recommendations include, developing gender awareness, specialized training, talking about gender, and gender and relationships. Building the representation of male mental health professionals in eating disorder treatment may reduce stigma and myths about these disorders and have positive impacts on clients across genders.

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)0.019
metaresearch head score (Gemma)0.030
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.030
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0110.004
Scholarly communication0.0060.005
Open science0.0020.013
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0120.002

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 head0.029
GPT teacher head0.403
Teacher spread0.375 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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