Identifying personally modifiable factors for self-harm recovery in young people: A protocol for a systematic review
Bibliographic record
Abstract
<ns3:p>Background Self-harm is the most important predictor of suicide, one of the leading causes of death in young people globally. There is a dearth of studies examining the processes underpinning recovery for those who have self-harmed. In particular, there is a lack of studies identifying elements that people who have self-harmed can change or influence to improve their wellbeing i.e. personally modifiable factors. Identifying these factors is important for individuals and clinicians to reduce or cease self-harm behaviours and improve personal wellbeing. In addition, it is imperative to understand why implementing these personally modifiable factors may succeed or fail. This systematic review has two aims: firstly, to identify personally modifiable factors for self-harm recovery in young people; and secondly, to identify the implementation determinants (barriers and facilitators) of these factors. Methods The search strategy will employ terms relating to three concepts (i.e. ‘young people’, ‘self-harm’, and ‘personally modifiable factors’) and will use five databases for the search process: Medline, CINAHL, APA PsycInfo, Embase, and Web of Science. At least two independent reviewers will conduct the screening process using eligibility criteria, followed by data extraction and quality assessment of the included studies. The mixed methods appraisal tool (MMAT) will be used for quality assessment. Inductive coding will be used to identify the personally modifiable factors and the Consolidated Framework for Implementation Research (CFIR) will be used to analyse and summarise the implementation determinants for these factors. Conclusion Identifying personally modifiable factors for self-harm recovery, and the barriers and facilitators underpinning their implementation, could inform the design of effective public health interventions to reduce self-harm in young people. Registration PROSPERO registration number CRD420250650920</ns3:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".