How to Prevent Suicide in Older Patients with a Neurocognitive Disorder: A Scoping Review Leading to the Development of a Clinical Guide for Healthcare Workers
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Healthcare professionals working with individuals living with neurocognitive disorders (NCD) express the need for training to prevent suicidal behaviors in this population. Accordingly, this paper describes the process used to develop a suicide prevention clinical guide for use in geriatric care settings. METHODS: The project involved three steps. First, a team of researchers conducted a scoping review of empirical studies on suicide among older adults with NCD, focusing on prevalence, risk and protective factors, assessment and practical interventions. Secondly, based on these findings, the team created a clinical guide that helps healthcare professionals assess needs and suicide risk and formulate action plans to improve well-being, ensure safety, and reduce the risk of suicide. RESULT: The guide was finalized after 18 months of deliberation. It enables professionals to structure their evaluation, so that no relevant aspect is overlooked, and protective factors are reinforced. It emphasizes shared responsibilities and interdisciplinary collaboration. It recommends that professionals conduct a personalized clinical assessment of unmet needs to reduce distress. During the third step, the guide was evaluated through a pilot study, involving post-training focus groups and interviews with professionals who used it in clinical practice. CONCLUSIONS: Participants' feedback was integrated into the final version of the Guide, and the results indicated that it helped dispel misconceptions about the low risk of suicide among patients with NCD, whose suicidality is frequently misinterpreted as mere disruptive behavior. Organizational barriers represent the main challenge professionals may face when using the Guide.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".