Mental Health Distress: Oncology Nurses’ Strategies and Barriers in Identifying Distress in Patients With Cancer
Bibliographic record
Abstract
BACKGROUND: Oncology nurses have an important role in identifying mental health distress; however, the research to date indicates that oncology nurses often do not accurately detect this distress. OBJECTIVES: The aim of this study is to explore oncology nurses' perspectives on indicators of distress in patients, the strategies they use in identifying these signs of distress, and the barriers they face in recognizing these indicators. METHODS: Twenty oncology nurses were interviewed. The study used the grounded theory method of data collection and analysis. FINDINGS: Nurses relied on a number of emotional and behavioral indicators to assess distress. Nurses reported that indicators of mental health distress often were expressed by patients or their caregivers. Strategies to identify distress were limited, with nurses reporting that their only method was directly asking the patient. Barriers to identifying distress included patients concealing distress, nurses' lack of training, and time constraints.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".