Religious coping and suicidal ideation as a predictive model for suicide attempts
Bibliographic record
Abstract
Background and objective: Religious coping can help understanding suicide in its prevention and intervention. The objective of this study was to investigate if religious coping and suicidal ideation could be used as a predictive model for suicide attempts.Methods: This is a survey and correlational delineation, carried out in southern Brazil with 260 adults assisted by the a public mental health service in two cities in northern Paraná from October 2020 to June 2021. For data collection, three instruments were used: The first one was a sociodemographic/clinical questionnaire to profile the participants; The second instrument was composed of 30 items dealing with impulsivity manifestations. The third instrument was used because it addresses coping and how people deal with everyday adversity through faith. Logistic regression analysis was used, through the SPSS®, v. 28. The dichotomous outcome variable was the suicide attempt, and the independent variables were a negative domain of the RCOPE scale. At first, three regression models were considered. A greater adherence happened only when the nominal variable “Have you ever tried to commit suicide?” qualified as an outcome variable.Results: The suicide attempt as an outcome and the other variables performing as predictive represented a higher sensitivity (96.8%) compared to the previous models. As the negative coping score was increased by 1 unit, there was also an increase in the chances of suicide attempting by 1.83 times (95% CI 1.11;3.00).Conclusions: It was discussed that religiosity has a positive impact on suicidal behavior, as it provides well-being/happiness, especially for those with mental disorders. This study would contribute to understanding the role and effects of religious coping in the process of counseling by healthcare workers. It is the first study to present predictive modelling for suicide attempt from negative religious coping. Hopefully, it would contribute to improve the process of counseling by mental health nurses and other healthcare workers.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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".