Gatekeeping in cancer clinical trials in Canada: The ethics of recruiting the “ideal” patient
Bibliographic record
Abstract
BACKGROUND: Perspectives of clinical trial (CT) personnel on accrual to oncology CTs are relatively absent from the literature. This study explores CT personnel's experience recruiting patients to oncology CTs. METHODS: A qualitative study design was utilized. In-depth, individual interviews with 12 oncology CT personnel were conducted, including six CT nurses and six physician-investigators. Interviews were digitally recorded and transcribed verbatim. Data were subjected to thematic and ethical analysis to identify key concepts and themes. RESULTS: CT personnel reported considering two ethical commitments in CT recruitment: maintaining trial integrity and ensuring patient autonomy through obtaining informed consent. The process of gatekeeping emerged as a way to navigate these ethical commitments during CT accrual. Gatekeeping was influenced by: (a) perceptions of patients' personal suitability for a trial, and (b) healthcare resources and infrastructure. CT personnel's discernment of personal suitability was influenced by patients' cognitive and mental health status, language and cultural background, geographic location, family support, and disease status. Three structural factors impacted gatekeeping: complexity of CTs, consent process, and time limitations in the healthcare system. CT personnel experienced most factors as constraints to accrual and gaining patients' informed consent. CONCLUSION: CT personnel discussed navigating ethical challenges in CT recruitment by offering enrollment to specific patient populations, exacerbating other ethical tensions. Systems-level strategies are needed to address barriers to ethical CT recruitment. Future research should investigate the role of policies and/or tools (eg, decision aids) to support patients and CT personnel's discussions about CT participation, promote more ethical recruitment, and potentially increase accrual.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Other design | low |
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 | 0.121 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.032 | 0.035 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".