Informing Eating Disorder Support Through Lived Experience
Bibliographic record
Abstract
An increase in eating disorder hospitalizations was observed amongst Canadian adolescents during COVID-19 public health restrictions. To help understand why this may have occurred, youth with lived experience of an eating disorder share their interpretations of these findings. This article, written by youth patient partners, provides insights into how unpredictable changes to daily routines and health system challenges during the COVID-19 pandemic might have influenced eating disorder hospitalizations. The increase in hospitalizations during the pandemic, combined with our lived experience advisory, underscores gaps in current approaches to supporting young people with eating disorders. We provide suggestions for clinicians, researchers, and policymakers stemming from our patient experiences in hopes that equitable, accessible, and patient-centered support can be prioritized to improve eating disorder-related care. This collaboration establishes a precedent for incorporating the voices of youth patient partners to better translate and mobilize research. These reflections serve as an example of how youth patient partner involvement can inspire future research, healthcare, and policy to advance care for those impacted by eating disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".