Nurses knowledge and attitudes to individuals who self-harm: A quantitative exploration
Bibliographic record
Abstract
Objective: Self-injury can be described as the deliberate destruction of the body without the intent to die, and is a distinct clinical presentation needing to be assessed separately from suicide and para-suicide. Nurses attitude to self-injury is a largely unexplored area particularly within Australia. The aim of this paper is to explore Australian general and mental health nurses’ attitudes towards self-injury taking into account their preparation as registered nurses (RNs) or enrolled nurses (ENs) and length of experience.Methods: This was a mixed methods exploratory design study. Phase one used a combination of two established surveys, the Self-Harm Antipathy Scale (SHAS) and the Attitudes Towards Deliberate Self-Harm Questionnaire (ATDSHQ). Nurses who were either RNs or ENs, mental health educated (MHE) or not, working in the area of mental health or emergency departments (ED) were recruited through a number of professional nursing organisations. A total of 172 nurses completed the phase one online questionnaire. The results of this survey are reported in this paper.Results: The key findings indicated a significant relationship between years of mental health nursing experience and mental health nursing qualification. A significant difference was noted in the knowledge level of self-injury between the mental health nurses who had a greater knowledge compared to those who were not mental health educated. Lastly, the attitudes of nurses to self-injury were generally found to be positive.Conclusions: These results extend much of what is in the literature on knowledge, attitudes and beliefs of nurses to non-suicidal self-injury (NSSI) and place these results in an Australian context. Further research to assess the effectiveness of increased education and community engagement should be undertaken.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".