SUICIDE PREVENTION: THE EXPERIENCES OF RECURRENT SUICIDE ATTEMPTERS (A PHENOMENOLOGICAL STUDY)
Bibliographic record
Abstract
: Objective: Presently, the issue of attempted suicide now poses one of the major challenges facing the healthcare providers in Iran and other countries. Although in previous years, the number of people entering to hospital emergency departments after deliberately taking overdoses or injuring themselves has been steadily increased in Iran, however, there is less attention to the issue of suicide prevention. The main aim of this study is to understand the experiences of those who re-attempted suicide, along with regarding suicide prevention. Methods: A qualitative phenomenological approach was applied. Purposeful samples of 12 patients who had a history of attempted suicide and were able to attend and respond to questions were recruited. Data was gathered by means of an in-depth semi-structured interview with each subject separately. The analysis of the data was conducted using the phenomenological analytic method defined by Colaizzi. Results: Over all, 667 descriptive codes were extracted, which were later reduced to 36 interpretative codes and then to 8 explanatory codes. Finally, four fundamental constructs of structural factors, personal factors, caring institutions and social networks were identified. Conclusion: The experiences of the participants showed that although individual factors are important and could influence suicide prevention, the structural and socio-cultural contexts which are out of individuals control are significant as well.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".