Factors in Making the Decision to Forgo Conventional Cancer Treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore why and how patients with cancer decide to forgo conventional cancer treatments in favor of alternative treatments and which factors influence such decisions. DESCRIPTION OF STUDY: Due to the exploratory nature of the study, this was a qualitative study using focus groups and in-depth interviews in a convenience sample of patients. All patients had received diagnoses of cancer and had refused one or more conventional treatments offered to them by their cancer healthcare professionals. RESULTS: Thirty-one persons with cancer, widely varying in age and tumor sites, volunteered to take part in the study. Of these, 12 refused all conventional treatment, 13 refused most or some of the treatments recommended, and 6 discontinued conventional treatment. The decision-making model, which emerged from the data, identifies several groups of variables. These include factors that predispose participants to the decision to forgo conventional treatment(s), such as having a close relative or friend who has died from cancer when receiving conventional treatment; experiences around the diagnosis; and factors relevant after the diagnosis, such as beliefs, need for control, side effects of conventional cancer treatment, and communication with physicians. Last, perceived outcomes of the decision proved to be an important theme in the focus groups and interviews. CLINICAL IMPLICATIONS: Patients with cancer may benefit from counseling to help them explore the difference between their diagnosis and treatment plan and those of family members or friends who died of cancer while receiving conventional treatment. Counseling also may be helpful in resolving emotional issues underlying the decision to forgo treatment. Last, patients should have access to healthcare professionals, including physicians and counselors, who would assist them with their decision making without judging or intimidating them.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it