Oncology nurses’ experiences regarding patients’ use of complementary and alternative therapies
Bibliographic record
Abstract
In their search for information and in making decisions about complementary and alternative therapies, patients will turn to oncology nurses. How oncology nurses respond to the patient's questions or comments can have an impact on the decision a patient makes about pursuing a particular therapy or whether the patient feels supported. The impetus for this work was the desire to understand how oncology nurses are responding to the patient trend of using complementary and alternative therapies. Twenty-eight nurses were interviewed over the telephone and a content analysis was completed from the transcribed interviews. The nurses who participated in this study regularly engaged in conversations with patients about complementary therapies and were aware of the reasons patients pursued these therapies. Conversations about alternative therapies occurred less frequently, but often created turmoil for the nurse. The nurses thought they had a role in maintaining an open dialogue about therapies, but felt their knowledge about particular therapies was limited. Obtaining information was a challenge and they often learned about specific therapies from patients and the popular media. Turmoil arose for nurses most often with regards to patients pursuing ingested therapies or alternative therapies. Nurses suggested complementary therapies to patients, but usually waited for patients to raise the topic of alternative therapies. Providing support to patients, whatever course they are choosing to pursue, was seen as an important nursing role.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".