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Record W3096888122 · doi:10.3389/fpsyt.2020.587321

Education Students' Stigma Toward Mental Health Problems: A Cross-Cultural Comparison

2020· article· en· W3096888122 on OpenAlexaffabout
José Gallego, Adolfo J. Cangas, José M. Aguilar-Parra, Rubén Trigueros, Noelia Navarro Gómez, Blanca Galván, Konstantin Smyshnov, Melanie Gregg

Bibliographic record

VenueFrontiers in Psychiatry · 2020
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Winnipeg
FundersAgencia Estatal de InvestigaciónMinisterio de Ciencia, Innovación y Universidades
KeywordsStigma (botany)Multivariate analysis of variancePsychologyMental healthCultural diversitySocial stigmaClinical psychologySocial psychologyPsychiatryMedicinePolitical scienceFamily medicine

Abstract

fetched live from OpenAlex

One of the main obstacles to integrating individuals with severe mental disorders into society today is the stigma directed at them. Although breakthroughs in treatment have been made in recent years, many professionals continue to admit that they do not possess enough training to combat this problem. Considering this situation, the present study analyzes the existing stigma among University Education students in three countries with different education systems and cultures, namely Spain, Russia, and Canada. A total of 1,542 students from these three countries participated in the study. ANOVA, MANOVA, and Multigroup Confirmatory Factor Analysis were applied in the data analysis. The results showed that the highest rates of stigma were in Spain and the lowest were in Canada, while Russia displayed intermediate values. This work addresses the relevance of these results, the influence that cultural difference may have on education policies, and the need to implement anti-stigma programs in countries like Spain, which has a relatively high level of social stigma and where these programs are practically not applied at all.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.433
Teacher spread0.394 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations26
Published2020
Admission routes2
Has abstractyes

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