Recruiting Terminally Ill Patients into Non-Therapeutic Oncology Studies: views of Health Professionals
Bibliographic record
Abstract
BACKGROUND: Non-therapeutic trials in which terminally ill cancer patients are asked to undergo procedures such as biopsies or venipunctures for research purposes, have become increasingly important to learn more about how cancer cells work and to realize the full potential of clinical research. Considering that implementing non-therapeutic studies is not likely to result in direct benefits for the patient, some authors are concerned that involving patients in such research may be exploitive of vulnerable patients and should not occur at all, or should be greatly restricted, while some proponents doubt whether such restrictions are appropriate. Our objective was to explore clinician-researcher attitudes and concerns when recruiting patients who are in advanced stages of cancer into non-therapeutic research. METHODS: We conducted a qualitative exploratory study by carrying out open-ended interviews with health professionals, including physicians, research nurses, and study coordinators. Interviews were audio-recorded and transcribed. Analysis was carried out using grounded theory. RESULTS: The analysis of the interviews unveiled three prominent themes: 1) ethical considerations; 2) patient-centered issues; 3) health professional issues. Respondents identified ethical issues surrounding autonomy, respect for persons, beneficence, non-maleficence, discrimination, and confidentiality; bringing to light that patients contribute to science because of a sense of altruism and that they want reassurance before consenting. Several patient-centered and health professional issues are having an impact on the recruitment of patients for non-therapeutic research. Facilitators were most commonly associated with patient-centered issues enhancing communication, whereas barriers in non-therapeutic research were most often professionally based, including the doctor-patient relationship, time constraints, and a lack of education and training in research. CONCLUSIONS: This paper aims to contribute to debates on the overall challenges of recruiting patients to non-therapeutic research. This exploratory study identified general awareness of key ethical issues, as well as key facilitators and barriers to the recruitment of patients to non-therapeutic studies. Due to the important role played by clinicians and clinician-researchers in the recruitment of patients, it is essential to facilitate a greater understanding of the challenges faced; to promote effective communication; and to encourage educational research training programs.
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How this classification was reachedexpand
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 | 0.069 | 0.588 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".