Alleviating existential distress of cancer patients: can relational ethics guide clinicians?
Bibliographic record
Abstract
Most people have a heightened awareness of death at the moment they receive a cancer diagnosis. Medical treatment attempts to demystify and manage death, yet surprisingly, care that alleviates existential distress is the least provided psychosocial care. A review of empirical research [quantitative and qualitative studies (n = 85) and seven literature reviews] was conducted to explore the experiences of clinicians (primarily nurses) working with cancer patients who experience existential distress. This paper summarizes clinicians' experiences with cancer patients who face the threat of mortality. Given that the majority of literature was found to be in nursing, emphasis in this paper tends to be on nurses' experiences. However, findings are suggested to have implications for other clinicians who deal with similar concerns. A lens of relational ethics was inductively found to organize and highlight problems and gaps that originate from interpersonal concerns. This paper describes four themes requiring further research and education related to existential distress: engagement, embodiment, environment and mutual respect. Implications for oncology care are suggested at the micro-, meso- and macro-levels to encourage clinicians to ethically respond to patients' existential distress needs.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".