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Record W4388538992 · doi:10.1080/09515070.2023.2277318

Relationship between intolerance of uncertainty and mental wellness: a cross-cultural examination

2023· article· en· W4388538992 on OpenAlexaff
Hawra Al-Khaz’Aly, Shayndel Jim, Chye Hong Liew, Gabriel Zamudio, Ling Jin

Bibliographic record

VenueCounselling Psychology Quarterly · 2023
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsMcGill UniversityUniversity of Calgary
Fundersnot available
KeywordsModerationLife satisfactionPsychologyChinaMental healthDepressive symptomsClinical psychologyCross-culturalSocial psychologyPsychiatryCognition

Abstract

fetched live from OpenAlex

The majority of research on mental wellness has been focused on Western societies, while little is known about cross-cultural differences of mental wellness and factors associating with mental wellness. The present cross-cultural research examined the relationship between intolerance of uncertainty (IU) and mental wellness among groups recruited from the United States (US), Mexico, and China. A total of 1,198 participants (359 from the US, 432 from Mexico, 407 from China; 55.50% female, 44.50% male) completed the survey study. The moderation effect of country of membership in the relationship between IU-depressive symptoms/life satisfaction was investigated through PROCESS Model 1. Our results revealed that country of membership did not moderate the relationship between IU and depressive symptoms, indicating that the IU-depressive symptom link is culturally invariant. On the other hand, country of membership statistically significantly moderated the relationship between IU and life satisfaction (p < .001, R2 = .10). Specifically, greater IU was inversely associated with life satisfaction amongst US and Mexican individuals, but not for Chinese individuals. Findings suggest cross-cultural variations in the relationship between IU and life satisfaction. Implications, limitations, and future directions were offered.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.640

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 studies0.0000.000
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 head0.122
GPT teacher head0.435
Teacher spread0.313 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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