Decision-making about complementary and alternative medicine by cancer patients: integrative literature review.
Bibliographic record
Abstract
BACKGROUND: Patients with cancer consistently report conflict and anxiety when making decisions about complementary and alternative medicine (CAM) treatment. To design evidence-informed decision-support strategies, a better understanding is needed of how the decision-making process unfolds for these patients during their experience with cancer. We undertook this study to review the research literature regarding CAM-related decision-making by patients with cancer within the context of treatment, survivorship, and palliation. We also aimed to summarize emergent concepts within a preliminary conceptual framework. METHODS: We conducted an integrative literature review, searching 12 electronic databases for articles published in English that described studies of the process, context, or outcomes of CAM-related decision-making. We summarized descriptive data using frequencies and used a descriptive constant comparative method to analyze statements about original qualitative results, with the goal of identifying distinct concepts pertaining to CAM-related decision-making by patients with cancer and the relationships among these concepts. RESULTS: Of 425 articles initially identified, 35 met our inclusion criteria. Seven unique concepts related to CAM and cancer decision-making emerged: decision-making phases, information-seeking and evaluation, decision-making roles, beliefs, contextual factors, decision-making outcomes, and the relationship between CAM and conventional medical decision-making. CAM decision-making begins with the diagnosis of cancer and encompasses 3 distinct phases (early, mid, and late), each marked by unique aims for CAM treatment and distinct patterns of information-seeking and evaluation. Phase transitions correspond to changes in health status or other milestones within the cancer trajectory. An emergent conceptual framework illustrating relationships among the 7 central concepts is presented. INTERPRETATION: CAM-related decision-making by patients with cancer occurs as a nonlinear, complex, dynamic process. The conceptual framework presented here identifies influential factors within that process, as well as patients' unique needs during different phases. The framework can guide the development and evaluation of theory-based decision-support programs that are responsive to patients' beliefs and preferences.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".