Strategies and Barriers in Addressing Mental Health and Suicidality in Patients With Cancer
Bibliographic record
Abstract
PURPOSE: To identify how oncology nurses address mental health distress and suicidality in patients, what strategies they employ in treating this distress, and the barriers they face in addressing distress and suicidality in patients with cancer. PARTICIPANTS & SETTING: 20 oncology nurses at two cancer centers in Israel were interviewed. METHODOLOGIC APPROACH: The grounded theory method of data collection and analysis was employed. FINDINGS: Strategies used in addressing patients' mental health distress were being emotionally available, providing practical support, treating physical symptoms, and referring to counseling. Strategies in addressing suicidality were assessing the situation, offering end-of-life or palliative care, treating physical symptoms, and referring for assessment. Barriers to addressing distress were lack of training, stigma, workload or lack of time, and limited availability and accessibility of mental health resources. Barriers in addressing suicidality were lack of knowledge and training, patient reluctance to receive care, and lack of protocol. IMPLICATIONS FOR NURSING: Developing guidelines for addressing and responding to mental health distress and suicidality is essential to improving patients' quality of life and reducing disease-related morbidity and mortality. Reducing mental healthcare stigma for patients is critical.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".