MétaCan
Menu
Back to cohort
Record W4200607795 · doi:10.31234/osf.io/szg5d

Stigmatizing our own: Self-relevant research (Me-search) is common but frowned upon in clinical psychological science

2021· preprint· en· W4200607795 on OpenAlexaff
Andrew Devendorf, Sarah E. Victor, Jonathan Rottenberg, Rose Miller, Stephen P. Lewis, Jennifer J. Muehlenkamp, Dese’Rae L. Stage

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Guelph
FundersTexas Tech University
KeywordsMental healthPsychologyPrejudice (legal term)Diversity (politics)Clinical psychologyMental illnessMedical educationSocial psychologyPsychotherapistMedicineSociology

Abstract

fetched live from OpenAlex

How often do clinical psychologists have a lived experience with, or close connection, to their research? Does the field of psychology accept this “me-search”? We undertook the first investigation of self-relevant research (SRR; “me-search”) and attitudes towards SRRers in a representative North American sample (N = 1,776) of faculty, graduate students, and others affiliated with doctoral programs in clinical, counseling, and school psychology. Over 50% of participants had conducted SRR, and those from minorized backgrounds were more likely to conduct SRR. When judging experimentally manipulated vignettes, those who had not engaged in SRR made more stigmatizing judgements of SRR and SRR disclosure than those who engaged in SRR. Psychologists and trainees had more negative attitudes towards SRR on mental health topics (suicide, depression, schizophrenia) than physical health topics (cancer). We discuss how prejudice toward SRR and mental illness negatively impacts ongoing diversity and inclusion efforts from within clinical psychological science.

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)0.081
metaresearch head score (Gemma)0.190
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0810.190
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.026
Scholarly communication0.0080.007
Open science0.0010.012
Research integrity0.0030.005
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 head0.371
GPT teacher head0.603
Teacher spread0.232 · 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.

Study designQualitative
DomainIncentives
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

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicMental Health Treatment and AccessFrench-language works237,207