Motivation to Consent and Adhere to the FORT Randomized Controlled Trial
Bibliographic record
Abstract
The aim of this qualitative study was to identify the motivational factors that influence cancer survivors to participate and adhere to the fear of cancer recurrence (FCR) FORT randomized controlled trial (RCT). Fifteen women diagnosed with breast and gynecological cancer who took part in the FORT RCT were interviewed about their experience to consent and adhere to the trial. The transcribed interviews were content analyzed within a relational autonomy framework. The analysis revealed that the participants' motivation to consent and adhere to the FORT RCT was structured around thirteen subthemes grouped into four overarching themes: (1) Personal Influential Factors; (2) Societal Motivations; (3) Structural Influences; and (4) Gains in Emotional Support. The unique structures of the trial such as the group format, the friendships formed with other participants in their group and with the group leaders, and the right timing of the trial within their cancer survivorship trajectory all contributed to their motivation to consent and adhere to the FORT RCT. While their initial motivation to participate was mostly altruistic, it was their personal gains obtained over the course of the trial that contributed to their adherence. Potential gains in emotional and social support from psycho-oncology trials should be capitalized when approaching future participants as a mean to improve on motivations to consent and adhere.
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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 | 0.130 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".