Patient perspectives on an intervention after suicide attempt: The need for patient centred and individualized care
Bibliographic record
Abstract
BACKGROUND: Many types of intervention exist for suicide attempters, but they tend not to sufficiently consider patient's views. AIM: To identify useful components of a previously evaluated intervention after a suicide attempt from the patient's views and to better understand the process of recovery. METHOD: Forty-one interviews with suicide attempters were qualitatively analysed. Views of participants (i) on the components of the intervention (nurse case-management, joint crisis plan, meetings with relatives/network and follow-up calls) and (ii) their recovery were explored. The material was analysed by means of thematic analysis with a deductive-inductive approach. RESULTS: Participants valued the human and professional qualities of the nurse case-manager, and appreciated follow-up calls and meetings. However, their views diverged regarding for instance frequency of phone calls, or disclosing information or lack thereof. Interpersonal relationship, suicide attempters' own resources and life changes emerged as core recovery factors. DISCUSSION: The study highlights the figure of an engaged clinician, with both professional and human commitment, aware that some suicide attempters put more emphasis on their own resources than on delivered health care. CONCLUSIONS: Interventions should consider the clinician as the cornerstone of the tailored care valued by suicide attempters.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".