Characteristics and Features of Non-Suicidal Self Injury among Psychiatric Population
Bibliographic record
Abstract
The present study aims to investigate the characteristics, features, and other contributing factors of non-suicidal self-injury in the psychiatric population. The objective is to explore the features and reasons behind non-suicidal self-injurious behaviors and whether they have any intention of suicide. A descriptive research design was used to collect the data of 70 participants (Men=27 and Women=43) with age ranging from 20-35 (Mean age=27.3, S.D=4.9) through purposive sampling technique. Data were collected from a number of Government and Private Hospitals using the Ottawa Self-injury Inventory (OSI). Data were analyzed using SPSS. Results revealed that NSSI started at the age of 17 years with the most frequent body parts used to be injured were abdomen, neck, throat, etc. and the most frequent method used was cutting, taking too much medication, and hitting. According to the results, rejection, abandonment, failure, and loss were the most important factors to trigger non-suicidal self-injury. Present research reveals some of the major factors and features of non-suicidal self-injury along with its contributing factors, such as methods used to harm oneself and most harm body parts along with its addictive nature. The findings revealed the importance of the study by helping mental health professionals to understand the reason behind.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".