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Record W2602223080 · doi:10.1177/009164711504300405

God Images and Resilience: A Study of Vietnamese Immigrants

2015· article· en· W2602223080 on OpenAlexaffabout
Thanh Tu Nguyen, Christian R. Bellehumeur, Judith Malette

Bibliographic record

VenueJournal of Psychology and Theology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsVietnameseImmigrationPsychologySocial psychologyPsychological resiliencePerspective (graphical)SociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This article examines the relationship between resilience and the God images of Vietnamese immigrants living in Canada. In earlier literature, little attention was paid to the way that these immigrants may have used spiritual components as resources in helping them endure struggle and become resilient (i.e., well-adapted, resourceful, and filled with a sense of purpose). In the current study, we examined the above link using an object relations’ perspective on God images and hypothesized that a more positive God image would be related to greater resilience. One hundred twenty-nine participants completed a demographic questionnaire, the Questionnaire on God Image (QGI), and the Connor-Davidson Resilience Scale (CD-RISC). Correlation and regression analyses showed that positive God images (e.g., those characterized by love, comfort, protection, and trust) were not only positively associated with a higher degree of resilience but also helped predict resilience among the Vietnamese immigrants we surveyed. We discuss various cultural implications of these findings for theory, research, and practice and make recommendations for future research.

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)0.003
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 score0.218
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.410
Teacher spread0.371 · 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

Citations14
Published2015
Admission routes2
Has abstractyes

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