Patient knowledge, experiences and preferences regarding retinoblastoma and research: A qualitative study
Bibliographic record
Abstract
BACKGROUND: We launched a patient engagement strategy to facilitate research involvement of the retinoblastoma (childhood eye cancer) community in Canada. To inform our strategy, we aimed to uncover the experiences with retinoblastoma, knowledge of retinoblastoma and research engagement among retinoblastoma survivors and parents. METHODS: Focus groups were held in Toronto and Calgary, including both in-person and remote participants (via videoconference). Discussions centred on experience with retinoblastoma, knowledge of the disease and engagement with research. Focus group transcripts were evaluated by inductive thematic analysis. RESULTS: Four focus groups (3 in Toronto, 1 in Calgary) were held with a collective total of 34 participants. Retinoblastoma had a substantial impact on the life of participants, but overall, patients reported being able to adapt and persevere. Experiential knowledge of retinoblastoma was identified as distinct from the theoretical knowledge held by their clinicians. Participants indicated they often acted as a knowledge broker, communicating information about the cancer to their social networks. Participants were willing to engage in research as partners, but recognized barriers such as time and appropriate training. CONCLUSIONS: Patients view their experiential knowledge of retinoblastoma as valuable to improving care and directing research. There is a unique role for research engagement in meeting the educational needs of patients.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".