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Record W2605880637 · doi:10.5430/jnep.v7n9p85

Self-care study in the Iranian medical students based on Islamic thought

2017· article· en· W2605880637 on OpenAlexvenueno aff
Morteza Darabinia, Alimorad Heidari Gorji, Shirzad Gholami

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
FundersMazandaran University of Medical Sciences
KeywordsStipulationIslamPharmacyPsychologyMeditationMedical educationSample (material)Health careSimple random sampleMedicineNursingTheologyPolitical scienceEnvironmental healthPhilosophy

Abstract

fetched live from OpenAlex

Objective: This study aimed to investigate Islamic Self-care in Iranian medical students.Methods: In this descriptive study that was done in 2015, students of the Mazandaran University of Medical Sciences (faculties of health, paramedical, pharmacy, medicine and dentistry) were selected through classified random sampling. Study sample was calculated 269 from 900 students based on Krejcie and Morgan’s table. Data gathering was done using demographic and a standardized questionnaire about Islamic Self-care.Results: In this study, 269 students were entered to study. The mean (SD) score of Stipulation, Meditation, Calculation and Expostulation of students were 9.82 (2.35), 10.04 (2.85), 8.74 (2.65) and 7.64 (2.48) respectively. Results shows that correlation between all the components of the Self-care are positive and significant (p < .001).Conclusions: It is worthy that further study will be aimed to investigate ways to strengthen it, as well as explaining the benefits of Islamic Self-care among students with the theoretical and practical examples.

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)0.002
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 score0.600
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 head0.073
GPT teacher head0.525
Teacher spread0.452 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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