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Record W4310104798 · doi:10.3998/jmmh.1553

The Correlation Between Religiosity and Death Anxiety During the COVID-19 Pandemic in Palestine

2022· article· en· W4310104798 on OpenAlexaff
Fayez Mahamid, Priscilla Chou, A. Mansour

Bibliographic record

VenueJournal of Muslim Mental Health · 2022
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of Guelph-Humber
Fundersnot available
KeywordsReligiosityDeath anxietyAnxietyPandemicPsychologyClinical psychologyPalestineCoronavirus disease 2019 (COVID-19)PsychiatryMedicineSocial psychologyDiseaseInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Previous studies have demonstrated that religiosity may be a predictive factor for anxiety related to death among adults amid the COVID-19 pandemic; however, current study variables have not been examined among Palestinians. This correlational study was the first to test the association between religiosity and death anxiety among Palestinians in the middle of the COVID-19 pandemic. Sample data consisted of 548 Palestinian adults. Data was collected through online advertisements, e-mail, and social media campaigns. Findings confirmed that death anxiety negatively correlated with religiosity (r = −.31, p<0.01). Regression analysis for predicting anxiety related to death determined that religiosity accounted for statistical and significant variance in death anxiety (B= −.191, SE=.040, β=−.20). It is recommended that further studies be conducted to explore the correlation between our current study variables and other related variables. This study also recommends the development of intervention programs to decrease death anxiety during pandemics or crises and enhance the protective factors of individuals.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.369
Teacher spread0.325 · 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.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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