Storytelling in Care: Leveraging Narrative Identity and Suicide Narratives for Advanced Suicide Risk Assessments
Bibliographic record
Abstract
OBJECTIVE: To explore how integrating Narrative Identity (NI) theory and the Suicidal Narrative (SN) framework into nursing practices can enhance suicide risk assessments and therapeutic engagement, promoting resilience, hope, and recovery among patients. METHODS: This study reviews existing literature on NI and SN frameworks, examining their theoretical foundations and applicability in nursing. It analyzes how these frameworks improve understanding of patient suicidality through qualitative assessment of personal narratives and identifies practical steps for implementation in clinical settings. RESULTS: The integration of NI and SN into nursing practices has shown potential in improving the quality of suicide risk assessments. It enables nurses to gain a deeper, empathetic understanding of the factors influencing each patient's suicidality, fostering enhanced therapeutic engagement. Challenges such as time constraints and the need for specific training in narrative techniques are identified. CONCLUSIONS: Incorporating NI and SN into nursing assessments can significantly enrich the suicide risk assessment process, providing a more nuanced and empathetic approach that focuses on individual patient stories. However, effective implementation requires overcoming several barriers, including enhancing nurse training in narrative methods and adjusting clinical workflows to accommodate more in-depth patient interactions.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".