American Academy of Nursing Expert Panel Consensus Statement on leveraging equity in policy to improve recognition and treatment of mental health, substance use disorders, and nurse suicide
Bibliographic record
Abstract
Rates of nurse mental health and substance use disorders are high. Heightened by the COVID-19 pandemic, nurses are challenged to care for patients in ways that often jeopardize their own health and increase risks for their families. These trends exacerbate the epidemic of suicide in nursing underscored by several professional organization clarion calls to nurses' risk. Principles of health equity and trauma-informed care dictate urgent action. The purpose of this paper is to establish consensus among clinical and policy leaders from Expert Panels of the American Academy of Nursing about actions to address risks to mental health and factors contributing to nurse suicide. Recommendations for mitigating barriers drew from the CDC's 2022 Suicide Prevention Resource for Action strategies to guide the nursing community to inform policy, education, research, and clinical practice with the goals of greater health promotion, risk reduction, and sustainment of nurses' health and well-being are provided.
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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 | 0.114 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.022 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".