The Provision of Complementary, Alternative, and Integrative Medicine Information and Services: a Review of World Leading Oncology Hospital Websites
Bibliographic record
Abstract
Abstract Background Many cancer patients use complementary, alternative, and integrative medicine (CAIM) to improve their psychological and functional health. However, there is little known about the extent of CAIM information and services provided on oncology hospital websites. Methods This study reviewed public-facing websites to determine the degree of CAIM information provided and services offered by the world’s leading cancer hospitals in 2021. Nine authors extracted data from hospital websites individually and in triplicate, prior to meeting to revise data extractions. Data analysis was then performed by two authors to determine how many hospitals provided CAIM descriptions and offered CAIM services, and the extent of CAIM information provided. Results A total of 131 hospitals were included in this study. Of the eligible hospitals, 50.38% (n = 66) provided a theoretical description of CAIM; 48.09% (n = 63) provided a description of one or more CAIM therapies; 63.36% (n = 83) offered one or more CAIM therapies to cancer patients. The most common therapies described were the same as the most common therapies offered. These therapies are massage, special foods and diets, acupuncture, meditation, yoga, and creative outlets. While CAIM therapies were commonly offered, information surrounding the benefits and side effects associated with these therapies varied. Conclusions Due to the lack of CAIM standardization worldwide, there is a need for increased CAIM information provision on hospital websites to better inform and empower patients to make well-informed decisions about their health.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".