Developing Patient-Centered Inflammatory Bowel Disease–Related Educational Videos Optimized for Social Media: Qualitative Research Study
Bibliographic record
Abstract
BACKGROUND: Important knowledge gaps have been identified related to the causes and symptoms of inflammatory bowel disease (IBD) and medical treatments and their side effects. Patients with IBD turn to social media to learn more about their disease. However, such information found on the web is misleading and often of low quality. OBJECTIVE: This study aims to gain an in-depth understanding of the unmet educational needs of patients with IBD and to use the resulting insights to develop a collection of freely available, evidence-based educational videos optimized for dissemination through social media. METHODS: We used design thinking, a human-centered approach, to guide our qualitative research methodology. We performed focus groups and interviews with a diverse sample of 29 patients with IBD. Data collection was performed in 3 phases (inspiration, ideation, and implementation) based on IDEO design thinking. Phase 1 offered insights into the needs of patients with IBD, whereas phases 2 and 3 involved ideation, prototyping, and video testing. A thematic analysis was performed to analyze the resulting data. RESULTS: Patients emphasized the need for educational videos that address their challenges, needs, and expectations. From the data analysis, 5 video topics and their content emerged: IBD treatments' risks and benefits; how to be a self-advocate; how to stay healthy with IBD; how to cope with IBD; and educating families, friends, and colleagues about experiences of patients with IBD. CONCLUSIONS: Design thinking offers a deep understanding and recognition of the unmet educational needs of patients with IBD; this approach informed the development of 5 evidence-based educational videos. Future research will formally test and disseminate these freely available videos through social media.
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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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".