The Effect of Spiritual Well-Being on Social Nicotine Dependence, Alcohol Consumption, Internet Overuse and Gambling among Medical Students
Bibliographic record
Abstract
Background The purpose of this study is to examine the effect of the spiritual well-being on social nicotine dependence, alcohol consumption, use of internet and gambling among medical students (n＝271). Methods A cross-sectional survey was conducted on 271 medical students using self-administered questionnaires including the spiritual well-being scale, KTSND score, the alcohol use disorder identification test, the internet addiction test, and the Canadian problem gambling index (Korean Version). Results : There were significant negative relationships between spiritual well being and addictive behaviors such as social nicotine dependence (r＝-0.160, P＜0.05), alcohol consumption (r＝-0.357, P＜0.001), internet overuse (r＝-0.156, P＜0.01). High social nicotine dependence was related with high alcohol consumption (r＝ 0.317, P＜0.01), as well as internet overuse with gambling (r＝0.165, P＜0.01). Spiritual well being on was significantly related to alcohol consumption (β＝-0.244, P＜0.01) and use of internet (β＝-0.136, P＜0.01). This suggests the higher spiritual well being score the student has, the lower possibility of alcohol or internet overuse he or she tends to have. On the other hand, the impact of spiritual well being on social nicotine dependence or gambling were not significant. Conclusions The milestone of the current study is to provide the importance of better understanding of spiritual background of an individual, and to address the necessity of its holistic approach. Moreover, new spiritual counseling model and its healing program should be developed and validated before application.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".