Healthcare Professionals’ Experiences of Brief Admission by Self-Referral for Adolescents with Self-Harm at Risk of Suicide—A Qualitative Interview Study
Bibliographic record
Abstract
Brief Admission by Self-referral (BA), a standardized crisis intervention for individuals with repeated self-harm or suicidal behavior, was adapted for adolescents from 13 years in Region Skåne, Sweden, in 2018. BA aims to offer access to support based on autonomy and has been associated with reduced need of emergency care. Interviews with adolescents and legal guardians have pointed to BA as valuable and challenging, and professional support as key. This study aims to describe healthcare professionals' (HCPs) experiences of BA for adolescents with self-harm at risk of suicide. Interviews six years after implementation with fourteen HCPs from outpatient and inpatient psychiatric care were analyzed with qualitative content analysis. BA was perceived as valuable caretaking without taking over, promoting mental growth and agency by being brief and granting access. It was described as offering relief to families and HCPs, although perceived to lack a sufficient level of legal guardian participation. Key work processes included being grounded in leadership and outpatient treatment. Challenges included system inflexibility and fitting BA into the physical care context. The results of this study may support future implementation of BA for adolescents with self-harm at risk of suicide and add guidance around potential pitfalls.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".