Patient Decision-Making about Complementary and Alternative Medicine in Cancer Management: Context and Process
Bibliographic record
Abstract
OBJECTIVE: In this paper, we set out to describe the personal and social contexts of treatment decisions made by cancer patients concerning complementary and alternative medicine (CAM) and also the process through which cancer patients reach cam decisions throughout the cancer trajectory. METHODS: We selected and reviewed a variety of CAM decision-making models published in the past 10 years within the Canadian health literature. RESULTS: The cam decision-making process is influenced by a variety of sociodemographic, disease-related, psychological, and social factors. We reviewed four main phases of the cam decision-making process: Taking stock of treatment options. Gathering and evaluating CAM information. Making CAM decisions. Revisiting the cam decision. Immediately following diagnosis, cancer patients become interested in taking stock of the full spectrum of conventional and CAM treatment options that may enhance the effectiveness of their treatment and mediate potential side effects. Information about CAM is then gathered from numerous information sources that vary in terms of credibility and scientific legitimacy, and is evaluated. When making a decision regarding CAM options, patients attempt to make sense of the diverse information obtained, while acknowledging their beliefs and values. The CAM decision is often revisited at key milestones, such as the end of conventional treatment and when additional information about disease, prognosis, and treatment is obtained. CONCLUSIONS: The CAM decision-making process is a dynamic and iterative process that is influenced by a complex array of personal and social factors. Oncology health professionals need to be prepared to offer decision support related to CAM throughout the cancer trajectory.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".