Cocreating research priorities for anorexia nervosa: The Canadian Eating Disorder Priority Setting Partnership
Bibliographic record
Abstract
OBJECTIVE: The Canadian Eating Disorder Priority Setting Partnership was established to identify and prioritize the top 10 research priorities for females, 15 years or older, with anorexia nervosa, by incorporating equal input from those with lived experience, families, and healthcare professionals. METHOD: This project, which closely followed the James Lind Alliance guidelines, solicited research priorities from the Canadian eating disorder community by means of a five-step process including use of a survey, response collation, literature checking, interim ranking survey, and in-person prioritization workshop. RESULTS: The initial survey elicited 897 priorities from 147 individuals, with almost equal representation from all three stakeholder groups. From this, 603 responses aligned with the project objectives and were collapsed into 71 broader indicative questions. Based on available systematic reviews, 18 indicative questions were removed as they were considered answered by existing literature while 8 indicative questions were added from the recommendations of the reviews. In total, 61 indicative questions were ranked in an interim ranking survey, where 21 questions were prioritized as important by at least 20% of respondents. As a final step, 28 individuals from across Canada attended the prioritization workshop to coestablish the top 10 research priorities. DISCUSSION: Top priorities were related to treatment gaps and the need for more surveillance data. This systematic methodology allowed for a transparent and collaborative approach to identifying current priorities from both the service user and provider perspective. Wide dissemination is anticipated to promote work that is of high relevance to patients, families, and clinicians.
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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 | 0.214 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".