Oncology health care professionals' perspectives on the causes of mental health distress in cancer patients
Bibliographic record
Abstract
OBJECTIVE: To explore oncologists, social workers, and nurses' perceptions about the causes of their cancer patient's mental health distress. METHODS: The grounded theory (GT) method of data collection and analysis was used. Sixty-one oncology health care professionals were interviewed about what they perceived to be the causes of mental health distress in their patients. Analysis involved line-by-line coding and was inductive, with codes and categories emerging from participants' narratives. RESULTS: Oncology health care professionals were sensitive in their perceptions of their patients' distress. The findings were organized into three categories, namely, disease-related factors, social factors, and existential factors. Disease-related themes included side effects of the disease and treatment, loss of bodily functions, and body image concerns as causing patient's mental health distress. Social-related themes included socio-economic stress, loneliness/lack of social support, and family-related distress. Existential themes included dependence/fear of being a burden, death anxiety, and grief and loss. CONCLUSIONS: Oncology health care professionals were able to name a wide range of causes of mental health distress in their patients. These findings highlight the need to have explicit conversations with patients about their mental status and to explore their understanding of their suffering. A patient-centered approach that values the patient's conceptualization of their problem and their narrative to understanding their illness can improve the patient-provider relationship and facilitate discussions about patient-centered treatments.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".