Professional Care Experiences of Persons With Suicidal Ideation and Behavior: Model Development Based on a Qualitative Meta-Synthesis
Bibliographic record
Abstract
BACKGROUND: Health care professionals (HCPs) are challenged in caring for persons with suicidal ideation or behavior. For affected persons, professional care is essential, and being interviewed about their experiences can be stressful. The experiences of persons ideating or attempting suicide are essential to designing eHealth products to support them in crises and provide continuous care. OBJECTIVE: This study aimed to synthesize published qualitative research about how persons with suicidal thoughts or behavior experience inpatient or outpatient care. A model will be derived from the meta-synthesis to guide HCPs in their work with affected persons and provide a thorough needs assessment for eHealth development. METHODS: A qualitative meta-synthesis was conducted using an inductive approach, as proposed by Sandelowski and Barroso. The inclusion criteria were studies in English and German that dealt with persons who ideated or attempted suicide. Relevant articles were identified by searching the PubMed and Cinahl databases and by hand searching relevant journals and reference lists. The findings of each study were analyzed using initial and axial coding, followed by selective coding. Finally, a conceptual model was derived. RESULTS: In total, 3170 articles were identified in the systematic literature search. Articles were screened independently by 2 researchers based on the eligibility criteria. Finally, 12 studies were included. The central phenomenon observed among persons ideating or attempting suicide is their process from feeling unanchored to feeling anchored in life again. During inpatient and outpatient care, they experience being dependent on the skills and attitudes of HCPs. While helpful skills and attitudes support persons ideating or attempting suicide to reach their feeling of being anchored in life again, adverse interactions are experienced negatively and might lead to prolonging or maintaining the feeling of being unanchored in life. CONCLUSIONS: The study promotes a differentiated view of the experiences of persons ideating or attempting suicide. The derived conceptual model can guide HCPs in their work with affected persons to support affected persons during their recovery. Moreover, the conceptual model is useable as a springboard to develop eHealth solutions for crisis situations and long-term care.
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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 | 0.101 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".