Identifying Information Needs for Hirschsprung Disease Through Caregiver Involvement via Social Media: A Prioritization Study and Literature Review
Bibliographic record
Abstract
BACKGROUND: Patient and public involvement in health research is important to produce relevant and impactful results. OBJECTIVE: This paper aimed to prioritize and summarize Hirschsprung disease (HD)-related information needs among caregivers of children with HD and pediatric surgeons through partnership with a parent-initiated social media campaign. METHODS: We conducted a Web-based survey with the 2 stakeholder groups to identify information needs. The caregiver survey was conducted through a global Web-based community, and the surgeon survey was distributed to members of the Canadian Association of Paediatric Surgeons (CAPS). We conducted a literature review to identify evidence on the prioritized topics. RESULTS: Our findings showed that 54.9% (89/162) of the individuals completed the caregiver survey and 23.8% (52/218 listed members) of the pediatric surgeons completed the survey distributed through CAPS. Only 20% (18/89) of the caregivers reported being very satisfied or satisfied with the current HD-related resources. A final prioritized list of information needs included bowel management, nutrition and growth, infection, perianal irritation, gastrointestinal pain, surgical diagnostics, and surgical complications. In total, 87 studies were included in the literature review, which included the following: 8 reviews, 2 randomized controlled trials, 74 cohort studies, and 3 practice guidelines. Two priority issues identified by caregivers had only a single study that met the inclusion criteria, whereas 1 topic had none. CONCLUSIONS: With caregiver and surgeon input, we identified 7 information priority areas related to HD. A review of the literature on the priorities found little evidence to support the development of high-quality guidelines. More research is necessary to meet the information needs related to HD as identified by stakeholders.
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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 | 0.022 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.042 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".