Development and preliminary evaluation of a genetics education booklet for retinoblastoma
Bibliographic record
Abstract
BACKGROUND: Parents and survivors of retinoblastoma often hold misconceptions about the disease and desire more extensive and detailed information about its genetic nature. The aim of this study was to co-develop and evaluate a genetic education booklet for retinoblastoma. METHODS: A human-centered design approach was employed, in which the study team consulted with clinician and patient knowledge user groups to design, produce, and refine an educational booklet. Over three phases of consultation, the study team met with each knowledge user group to review booklet prototypes and collect feedback for its further refinement. A preliminary evaluation using quantitative and qualitative methods was completed with six mothers of children with retinoblastoma. RESULTS: The iterative, phased design process produced an educational booklet rich in images and stories, with complex genetic topics described in simplified terms. The preliminary evaluation showed an average improvement in knowledge between pre- and post-test questionnaire of 10%. Participants were satisfied with content and comprehensiveness of the information included in the booklet. CONCLUSION: A novel educational tool for families affected by retinoblastoma was developed through collaboration with health care and patient knowledge users. Preliminary evaluation results indicate it is feasible to implement and study the booklet in a prospective, pragmatic trial to evaluate its efficacy.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".