Decision-making and autonomy among participants in early-phase cancer immunotherapy trials: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Participants considering early-phase cancer clinical trials (CTs) need to understand the unique risks and benefits prior to providing informed consent. This qualitative study explored the factors that influence patients' decisions about participating in early-phase cancer immunotherapy CTs through the ethical lens of relational autonomy. METHODS: Using an interpretive descriptive design, interviews were conducted with 21 adult patients with advanced cancer who had enrolled in an early-phase CT. Data was analyzed using relational autonomy ethical theory and constant comparative analysis. RESULTS: The extent to which participants perceived themselves as having a choice to participate in early-phase cancer immunotherapy CTs was a central construct. Perceptions of choice varied according to whether participants characterized their experience as an act of desperation or as an opportunity to receive a novel treatment. Intersecting psychosocial and structural factors influenced participants' decision making about participating in early-phase cancer immunotherapy trials. These relational factors included: (1) being provided with hope; (2) having trust; (3) having the ability to withdraw; and (4) timing constraints. CONCLUSIONS: Findings highlight the continuum of perceived choice that exists among patients with cancer when considering participation in early-phase cancer immunotherapy CTs. All participants were interpreted as exhibiting some degree of relational autonomy within the psychosocial and structural context of early-phase CT decision making. This study offers insights into the intersection of cancer care delivery, personal beliefs and values, and established CT processes and structures that can inform future practices and policies associated with early-phase cancer immunotherapy CTs to better support patients in making informed decisions.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".