Bibliographic record
Abstract
Research indicates that mental health staff experience shock, distress and powerlessness when supporting people who self-harm. Existing studies have focused on inpatient staff, neglecting the voices of those working in community settings. AIM: The aim of the current systematic review and thematic synthesis was to explore community staff experiences of supporting people who self-harm. METHODS: PsycINFO, MEDLINE, CINAHL and Web of Science were searched from inception to 4th February 2025. Searches found 3,035 records, of which twelve reporting on 82 participants were identified as meeting the inclusion criteria and included in the synthesis. The Critical Appraisal Skills Programme Qualitative Checklist was used to assess the quality of included studies. RESULTS: Studies took place in the UK, USA, Ireland and Canada. All studies were qualitative recruiting Counsellors, Community Mental Health Nurses, Psychotherapists and a Social Care Support Worker. Studies obtained an average quality rating score of 17.9 out of 20, with most studies being highly rated. Thematic synthesis yielded five themes; 1) Staff experience an all-consuming, long-lasting emotional whirlwind, 2) Staff feel stuck between a rock and a hard place, 3) Staff see systemic challenges as impacting their support of people who self-harm, 4) Staff carry the weight of many different responsibilities, 5) It is necessary for staff to have ways of coping. CONCLUSION: A lack of consensus in the conceptualisation of self-harm highlights a need for researchers’ transparency regarding their theoretical position. The findings highlight the challenging experience staff have when supporting people who self-harm, warranting a need for further support and training.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".