Impact of Negative Attribution Style on Non-Suicidal Self-Injurious Behavior among Homeless Individuals
Bibliographic record
Abstract
Non-suicidal self-injurious behaviors have been examined by researchers for several decades (Klonsky et al., 2016). Negative attribution style is considered as a possible contributing factor in explaining non-suicidal self-injurious behaviors. The current study was carried out to explore the impact of negative attribution style on such behaviors among conveniently selected 300 homeless individuals from Khyber Pakhtunkhwa (KPK). The individuals were approached and briefed about the research and data collection procedure. The participants completed the Attributional Style Questionnaire and Ottawa Self-injury Inventory along with the demographic sheet. Negative attribution style was found to be positively related to non-suicidal self-injurious behaviors. The results revealed that this style positively predicted such behaviors among homeless individuals. Furthermore, it explained 13.1% variation in this type of behavior. Both subcategories of this attribution style positively predicted non-suicidal self-injurious behaviors. The results also revealed that stability explained 15.5% variation, whereas globality explained 9.6% variation in such behaviors. Moreover, the study revealed significant gender-based differences with respect to self-injurious behaviors. Strategies can be developed to target negative attribution styles and to promote positive ones. Psychosocial intervention targeting non-suicidal self-injurious behaviors can be used in this regard.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".