From the conventional to the alternative: exploring patients’ pathways of cancer treatment and care
Bibliographic record
Abstract
BACKGROUND: Complementary and alternative medicine (CAM) use is widespread and on the increase among cancer patients. Most research to date has involved a cross-sectional snapshot of CAM use rather than an exploration into the longitudinal, nonlinear treatment trajectories that cancer patients develop. Our aim is to explore and describe different treatment and decision-making pathways that individuals develop after receipt of a diagnosis of either breast, colorectal, or prostate cancer. METHODS: The study was part of a larger mixed-methods pilot project to explore the feasibility of conducting a five-year international study to assess cancer patients' treatment pathways, including health care use and the perceived impact of different patterns of use on health outcomes over the course of one year. The results presented in this paper are based on the analysis of personal interviews that were conducted over the course of 12 months with 30 participants. RESULTS: Five pathways emerged from the data: passive conventional, self-directed conventional, cautious integrative, aggressive integrative, and aggressive alternative. Factors that shaped each pathway included health beliefs, decision-making role, illness characteristics, and the patient-practitioner relationship. CONCLUSIONS: The results of this examination of the longitudinal treatment and decision-making trajectory provide important information to support health care professionals in their quest for individualized, targeted support at each stage of the patient pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".