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Record W1965416702 · doi:10.5539/ijps.v4n1p150

Religiosity Orientations and Personality Traits with Death Obsession

2012· article· en· W1965416702 on OpenAlexvenueno aff
Hamzeh Salmanpour, Ali Issazadegan

Bibliographic record

VenueInternational Journal of Psychological Studies · 2012
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyNeuroticismReligiosityReligious orientationConscientiousnessPersonalityBig Five personality traitsSocial psychologyDevelopmental psychologyExtraversion and introversion

Abstract

fetched live from OpenAlex

The aim of the current study was to investigate the relationship and predictability of death obsession through religiosity orientations and personality traits. Sample included 484 (246 girls, 238boys) that had been chosen through random stratified sampling. In order to assess research instrument was death obsession scale (DOS), NEO personality inventory (NEO- FFI) and Allport religious orientation scale. Data were analyzed using correlation and stepwise regression analyses method and t-test.Results showed that the relationship between death obsessions with extrinsic orientation toward religion is positive whereas death obsession has a negative relation with intrinsic orientation toward religion. Also findings showed there is significant positive relationship between neuroticism and death obsession (r=0/42, p<0/01). Other dimensions at personality had negative relationship with death obsession. The greater negative relationship was between intrinsic orientation toward religion and conscientiousness dimension (r=-0/34, p<0/01). Of all research variables, extrinsic orientation toward religion and neuroticism was able to predict 19 present of variance of death obsession. In study of difference between two group female and male in death obsession results showed that significant difference between two groups (t=5/38, df=482, p<0/001).

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 score0.117
Threshold uncertainty score0.268

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 head0.137
GPT teacher head0.473
Teacher spread0.335 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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