Surveys of Cancer Patients and Cancer Health Care Providers Regarding Complementary Therapy Use, Communication, and Information Needs
Bibliographic record
Abstract
BACKGROUND: Complementary therapies (CTs) are increasingly utilized by cancer patients. Nonetheless, patients report insufficient support from health care practitioners (HCPs) and there is a general lack of patient-practitioner communication about CT use. Best care practices suggest that HCPs should address the needs of patients, including CT use. This study examined current practices of patients and HCPs as well as their interactions relating to CTs. METHODS: A total of 481 cancer outpatients and 100 HCPs completed questionnaires. Patient questions addressed CT use and information needs; HCP questions addressed knowledge, opinions and beliefs about complementary and alternative medicine. Patient-practitioner communication around CT was also examined. RESULTS: 47% of patients reported using CTs since diagnosis. Many commenced CT use to improve quality of life (65%) based on recommendations from family or friends. Patients acknowledged the need for trusted sources of information and would attend a hospital-based education program (72%). HCPs reported limited training about CTs but most (90%) expressed interested in receiving more training. The majority of HCPs (>80%) reported limited knowledge about the role of CTs in cancer care or evidence to support CT use. Questions about communication and interactions revealed that 80% of patients reported not having had an HCP speak to them about CTs. However, 63% of HCPs reported addressing CT use. CONCLUSION: Results demonstrate a need for improved CT education and training for patients and HCPs. increasing HCP knowledge and clinical skills will ensure patients' information needs about CTs are acknowledged and attended to, thereby providing safer and comprehensive cancer care.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".