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Record W2944827918 · doi:10.1080/13557858.2019.1620176

Perceived religious discrimination and mental health

2019· article· en· W2944827918 on OpenAlexafffund
Zheng Wu, Christoph M. Schimmele

Bibliographic record

VenueEthnicity and Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of VictoriaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsReligious discriminationMental healthPsychologyBuddhismSpiritualityHinduismEthnic groupPopulationSocial psychologyConfoundingReligious identityGeneral Social SurveyJudaismDemographySociologyReligiosityReligious studiesMedicinePsychiatryGeographyPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: Most knowledge on the health consequences of discrimination comes from studies on racial/ethnic minorities, and research on religious discrimination is rare. To address this gap in knowledge, we examine the relationship between religious discrimination and self-rated mental health (SRMH), focusing on the role of religious affiliation as well as religious participation and the importance of religion/spirituality. METHODS: = 27,104) from all 10 provinces. The outcome variable is SRMH. Using OLS regressions, we compare the consequences of religious discrimination across five major religious groups (Christian, Buddhist, Hindu, Jewish, and Muslim), controlling for racial status and other confounding variables, and examining moderating factors. RESULTS: Religious discrimination is harmful for the SRMH of all religious groups. Despite experiencing higher levels of religious discrimination, religious minorities have no worse SRMH than the Christian majority, with the exception of Buddhists, who fare worse. The magnitude of the relationship between religious discrimination and SRMH differs across religious groups. CONCLUSION: Religious discrimination is a threat to mental health, irrespective of religious affiliation. There is a need to disaggregate non-Christian groups into distinct groups in studies of religious discrimination.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.068
GPT teacher head0.422
Teacher spread0.354 · 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

Citations45
Published2019
Admission routes2
Has abstractyes

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