A qualitative study examining newly diagnosed breast cancer patients’ experiences of participating in the Alberta Moving Beyond Breast Cancer (AMBER) prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Decisions to participate in cancer trials are associated with uncertainty, distress, wanting to help find a cure, the hope for benefit, and altruism. There is a gap in the literature regarding research examining participation in prospective cohort studies. The aim of this study was to examine the experiences of newly diagnosed women with breast cancer participating in the AMBER Study to identify potential strategies to support patients' recruitment, retention, and motivation. METHODS: Newly diagnosed breast cancer patients were recruited from the Alberta Moving Beyond Breast Cancer (AMBER) cohort study. Data were collected using semi-structured conversational interviews with 21 participants from February to May 2020. Transcripts were imported into NVivo software for management, organization, and coding. Inductive content analysis was undertaken. RESULTS: Five main concepts associated with recruitment, retention, and motivation to participate were identified. These main concepts included: (1) personal interest in exercise and nutrition; (2) investment in individual results; (3) personal and professional interest in research; (4) burden of assessments; (5) importance of research staff. CONCLUSIONS: Breast cancer survivors participating in this prospective cohort study had numerous reasons for participating and these reasons could be considered in future studies to enhance participant recruitment and retention. Improving recruitment and retention in prospective cancer cohort studies could result in more valid and generalizable study findings that could improve the care of cancer survivors.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".