P200 Suicide Risk Assessment According to Best Practice Guidelines: The Development of a Chart Audit Performance Measure
Bibliographic record
Abstract
Background Best practice guidelines (BPGs) in suicide risk assessment documentation support nursing care of clients at risk for suicide. Investigation regarding nurses’ adherence to BPGs for suicide risk assessment documentation is minimal. Objectives In a mixed-methods study to investigate nurses’ knowledge of suicide risk assessment documentation, the researchers created a chart audit to measure nursing practice congruence with five recommendations from the suicide risk assessment BPG (RNAO, 2009). Methods Five recommendations, from the BPG: Assessment and Care of Adults at Risk for Suicidal Ideation and Behaviour (RNAO, 2009), were the benchmarks for the chart audit measure. Suicide risk indicators, as determined by the Minimum Data Set for Mental Health (MDS-MH) (Ontario Ministry of Health, 2011), were the criteria to identify charts of suicidal clients. The researchers integrated MDS-MH indicators with the five BPG recommendations and constructed compliance indicators that incorporated the Nurses Global Assessment of Suicide Risk (Cutcliffe & Barker, 2004). Results Five BPG recommendations, integrated with provincial suicide assessment criteria and a standardised suicide assessment scale produced a 3-point likert scale chart audit with 15 indicators. Possible ranges of scores for documentation congruence with the BPG are 0 to 30. Discussion This performance measure provides objective, proxy data to triangulate with nurses’ self-perception of suicide risk documentation and evaluate practice as per BPGs. Implications for Guideline Developers/Users A standardised instrument to monitor BPG practices can be used to inform implementation and education strategies.
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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 | 0.076 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".