BARRIERS TO SUICIDE RISK MANAGEMENT IN CLINICAL PRACTICE: A NATIONAL SURVEY OF ONCOLOGY NURSES
Bibliographic record
Abstract
Standards of practice identify the nurse's pivotal role in risk detection, assessment, intervention, and management of suicidal patients, but scant research explores the barriers that hinder this role. This study describes the analysis of barriers to suicide risk management from a survey of a random sample of members of a national organization, the Oncology Nursing Society (n = 1200), who participated in a descriptive study exploring nurses' knowledge and attitudes about suicide. The 454 (37%) respondents included respondents from the United States, Canada, and Puerto Rico. Instruments included a demographic inventory, the Suicide Opinion Questionnaire (SOQ), a suicide attitude measure (SUIATT), and a vignette of a suicidal patient. Nurses knew an average of 4.8 out of 9 suicide risk factors and 49.4% miscalculated the risk of suicide. In contrast with their moderate to high ratings of suicide risk, they indicated minimal interventions. Barriers to management of suicidal patients included deficits in skill, knowledge, referrals, patient teaching, advocacy, or consultation as well as participants' and religious/other values, uncomfortable feelings, personal experiences, and the weight of professional responsibility. Strategies for intervention include: suicide prevention education, consultation, values clarification, ethical analysis, and conflict resolution and psychosocial support to reduce barriers. Nurses are not alone in their request for more education about suicide prevention; this study confirms earlier research of psychologists and psychiatrists who report they need more education in suicide risk management.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".