Needs assessment and patient-guided development of a video-based diabetic retinopathy patient education tool
Bibliographic record
Abstract
Objective: To gain retina physicians’ and diabetic retinopathy (DR) patients’ perspectives on needs and opportunities in DR education, and then develop and pilot test an educational video. Design: This study utilised qualitative interview data for video creation, and interview and survey data for assessment. Setting: This study was conducted in a single large academic medical centre. Method: We conducted semi-structured interviews with attending retina physicians and DR patients (Cohort A) which were coded for themes about needs in DR patient education. Using these interviews, we designed and piloted a 6-minute user-centred animated video among a second patient cohort (Cohort B), who completed post-intervention interviews. Results: Four physicians and 14 DR patients participated in the study. Themes from Cohort A included accessible information, early management, lifestyle factors and emotional context. Physician themes included effective communication, visual information delivery and individual-level diabetes management. Cohort B commented on the subsequently created video’s improved accessibility, engagement and supplementation of their existing DR knowledge. Conclusion: Physicians and patients showed an interest in video education and identified unique educational needs. We used these insights to create a video that demonstrated positive patient uptake. Close attention to retina physicians’ and DR patients’ perspectives can offer a valuable approach in developing materials to increase patients’ health knowledge. Within the context studied, videos may be more accessible and engaging than the use of traditional print-based education materials.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".