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Record W4283458628 · doi:10.1177/17456916211072826

Leveraging the Strengths of Psychologists With Lived Experience of Psychopathology

2022· article· en· W4283458628 on OpenAlexaff
Sarah E. Victor, Jessica L. Schleider, Brooke A. Ammerman, Daniel E. Bradford, Andrew Devendorf, June Gruber, Lisa A. Gunaydin, Lauren S. Hallion, Erin A. Kaufman, Stephen P. Lewis, Dese’Rae L. Stage

Bibliographic record

VenuePerspectives on Psychological Science · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsUniversity of GuelphWestern University
FundersNational Institute of Mental HealthNational Institutes of Health
KeywordsPsychopathologyPsychologyLived experienceInclusion (mineral)PsychotherapistClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

Psychopathology is a common element of the human experience, and psychological scientists are not immune. Recent empirical data demonstrate that a significant proportion of clinical, counseling, and school psychology faculty and graduate students have lived experience, both past and present, of psychopathology. This commentary compliments these findings by leveraging the perspectives of the authors and signatories, who have personal lived experience of psychopathology, to improve professional inclusivity in these fields. By "coming out proud," the authors aim to foster discussion, research, and inclusion efforts as they relate to psychopathology experiences in psychological science. To that end, the authors describe considerations related to disclosure of lived experience, identify barriers to inclusion, and provide concrete recommendations for personal and systemic changes to improve recognition and acceptance of psychopathology lived experience among psychologists.

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)0.022
metaresearch head score (Gemma)0.033
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.057
Scholarly communication0.0140.013
Open science0.0020.026
Research integrity0.0050.014
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 head0.074
GPT teacher head0.377
Teacher spread0.303 · 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

Citations63
Published2022
Admission routes1
Has abstractyes

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