Complementary therapies for cancer patients: assessing information use and needs
Bibliographic record
Abstract
Many cancer patients seek complementary therapies (CTs) for cancer management; however, relatively little is known about patients' CT information seeking behaviour. Therefore, we assessed: 1) cancer patients' use of the types and sources of CT information; 2) their information preferences; and 3) their understanding of the phrase "scientific evidence or proof that a therapy works." We collected data from 404 patients attending the Tom Baker Cancer Centre (TBCC) in Calgary and 303 patients calling the Cancer Information Service (CIS) helpline. In most cases, patients wanted information on the safety of CTs, how CTs work and their potential side effects. Physicians and conventional cancer centres were the most desired sources of CT information, but relatively few patients obtained information via these sources. Although patients were aware of the meaning of scientific evidence, they often used information based on non-scientific evidence, such as patient testimonials. The creation of a supportive care environment in conventional cancer treatment centres, by providing CT information, may help address cancer patients' concerns and alleviate some of the stress that may have been caused by the cancer diagnosis.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".