The Nurses’ Global Assessment of Suicide Risk (NGASR): developing a tool for clinical practice
Bibliographic record
Abstract
Contemporary and established literature indicates that people with mental health problems are at a higher risk of suicide than the general population. Because suicide is a multifaceted, complex phenomenon, risk assessment within the mental health care system requires a pluralistic, multidimensional and multiprofessional response. While assessment tools may provide useful guidance, especially guarding against complacency and over confidence, the fundamental basis of risk assessment must involve a thorough examination of the personal, interpersonal and social circumstances of each individual. Such thorough and rigorous assessments, the authors of this paper would add, require a degree of 'clinical judgement'. As a rule, inexperienced members of mental health care staff should not be charged with the responsibility of conducting suicide risk assessments without sound mentorship. However, with the right support and assessment tool, the novice practitioner might develop the kind of clinical judgement necessary for this critical task. Accordingly, this paper traces the development of the Nurses' Global Assessment of Suicide Risk (NGASR). It illustrates the practice development context out of which the need for the tool arose; it outlines the key evidence that underpins the construction of the tool and it is described. It is important to point out that as yet, no wide scale, quantitative validation of the tool has been conducted. Therefore, at this point, the tool should be treated with a degree of appropriate caution. Nevertheless, the preliminary attempts that have been made to 'validate' or 'rate' the tool in practice are included. While acknowledging that any risk assessment tool represents only one aspect of the necessarily broader assessment of risk, the NGASR appears to provide a useful template for the nursing assessment of suicide risk, especially for the novice.
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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 | 0.121 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".