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Record W2606549845 · doi:10.1177/0011000016671411

Stigma of Seeking Psychological Services: Examining College Students Across Ten Countries/Regions

2017· article· en· W2606549845 on OpenAlexaffabout
David L. Vogel, Haley A. Strass, Patrick J. Heath, Fatima R. Al-Darmaki, Patrick Ian Armstrong, Makilim Nunes Baptista, Rachel E. Brenner, Marta Gonçalves, Daniel G. Lannin, Hsin-Ya Liao, Corey S. Mackenzie, Winnie W. S. Mak, Mark Rubin, Nursel Topkaya, Nathaniel G. Wade, Ying‐Fen Wang, Alina Zlati

Bibliographic record

VenueThe Counseling Psychologist · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStigma (botany)PsychologyHelp-seekingStructural equation modelingSocial psychologyAssertionSocial stigmaMental healthPsychiatryMedicine

Abstract

fetched live from OpenAlex

Stigma is an important barrier to seeking psychological services worldwide. Two types of stigma exist: public stigma and self-stigma. Scholars have argued that public stigma leads to self-stigma, and then self-stigma is the primary predictor of attitudes toward seeking psychological services. However, this assertion is largely limited to U.S. samples. The goal of this research was to provide a first step in understanding the relationship between public stigma, self-stigma, and attitudes toward seeking psychological services in international contexts ( N = 3,276; Australia, Brazil, Canada, Hong Kong, Portugal, Romania, Taiwan, Turkey, United Arab Emirates, and United States). Using structural equation modeling, we found that self-stigma mediated the relationship between public stigma and attitudes toward seeking services among college students in each country and region. However, differences in path strengths emphasize the need to pay attention to the role of public and self-stigma on attitudes toward seeking psychological services throughout the world.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.000
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 head0.113
GPT teacher head0.469
Teacher spread0.356 · 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

Citations99
Published2017
Admission routes2
Has abstractyes

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