A Study of the Correlation between Religious Attitudes and Quality Of Life in Students at Jahrom University of Medical Sciences in 2014
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: General health is not simply determined by whether or not an individual is sick, but is dependent on physical, mental and social factors too. One such important factor is an individual's religious inclination. The present study aims to explore the correlation between religious beliefs and quality of life in the students at Jahrom University of Medical Sciences. METHOD: This is a descriptive, cross-sectional study conducted in 2014. The sample consisted of 273 students who were randomly selected. Data were collected using Religious Attitude Questionnaire and a quality of life scale. The collected data were analyzed using Pearson's correlation coefficient and SPSS v. 23. RESULT: The students' average age was 21.36±2.15. The means of their quality of life scores and religious attitude scores were 87.23 and 146.31 respectively. The results of Pearson's correlation test showed that there was a significant relationship between quality of life and its subscales on one hand and religious attitude and its indexes on the other; in other words, the students' mental well-being was found to correlate with their religious beliefs. CONCLUSION: Since religious beliefs affect college students' mental well-being and quality of life, it is suggested that through organized education, students' religious awareness be raised.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".