Generating Priorities in Eating Disorder Research: Recommendations from Canadian Young Adults and Caregivers.
Bibliographic record
Abstract
Background: Consensus-building approaches have seldom been used to develop research priorities informed by young adults and caregivers with lived eating disorder (ED) experience. Objective: To identify the most important research priorities among Canadian young adults and caregivers affected by EDs. Method: Using the Nominal Group Technique (NGT), we recruited Canadian young adults (ages 18-29) with pediatric ED treatment experience and their caregivers to participate in two separate NGT panels. The panels consisted of four stages: silent generation, round robin sharing, discussion, and ranking of the generated priorities. Priorities were weighted using a points system and later added to produce a total score for young adults and caregivers. Informed by Qualitative Description, qualitative content analysis was conducted to analyze the discussion portion of the panels. Results: =10) panels generated 19 and 24 priorities, respectively. The most highly endorsed research priorities among young adults were to improve existing treatment models, to include underrepresented groups in ED research, and to provide more ED education for healthcare providers. Caregivers also endorsed the need for research to improve ED training for healthcare providers and for more robust standard operating procedures and best practices for EDs. In the discussion, participants shared high-level recommendations, frustrations with current ED care, and positive reflections on the NGT process. Conclusions: Future research opportunities based on these generated priorities have the potential to improve alignment and relevance between ED research being conducted and the needs of young adults and caregivers affected by EDs.
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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 | 0.215 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| 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".