“We're Cancer Care Nurses”
Bibliographic record
Abstract
Nurses play a key role in integrating palliative care into oncology. This project sought to better understand oncology nurses' perspectives about palliative care. Nurses from a community hospital were presented with a series of clinical scenarios and asked to comment on the appropriateness of palliative care in each case. A series of focus groups were held, inviting nurses' reflections about palliative care in relation to their practice. Nurses commenting on the clinical scenarios were unanimous that palliative care was appropriate in the most straightforward case: older adult, approaching the terminal phase of a cancer, having exhausted all curative treatment options, accepting death, wanting comfort, and contending with difficult symptoms. However, opinions on appropriateness varied in less straightforward cases, such as when patients did not accept death or when their cancer diagnosis was recent. In focus groups, nurses described a hybrid professional identity that integrates both oncology and palliative care. To them, this integration constituted the meaning of "cancer care." They further reflected on tensions they experience between their proximity to patients in everyday care and their (in)abilities to meet palliative care needs. Results suggest the need for stronger institutional supports of cancer nurses' palliative practice.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".