Food for thought: A dissonance between healthcare utilization costs and research funding for eating disorders in Canada
Bibliographic record
Abstract
In this commentary, we present the premise that, in Canada, mental illness research specific to eating disorders is underfunded, and many Canadians are suffering the consequences of this underinvestment. We highlight three critical aspects of eating disorders: 1) the increasingly common yet potentially life-threatening nature of eating disorders, with an onset usually during adolescence; 2) the challenges and costs to treating eating disorders, with a discussion of current hospital-related costs across Canada; and 3) the glaring discrepancy between the money spent on eating disorder diagnoses/treatment and the funding dollars granted for eating disorder research in Canada (i.e. only $0.70 per affected Canadian in 2018). Research funding per affected individual for other psychiatric and neurodevelopmental conditions are used as comparisons (e.g. $50.17 per affected Canadian with schizophrenia). We suggest that it is time to revolutionize treatment for individuals with eating disorders and use our resources in a more efficient and effective manner, using current neuroimaging and neuromodulation methods as promising examples. We conclude by emphasizing the need for increased research funding in the field of eating disorders in Canada, as the current research-related investments hinder progress in developing neuroscientifically-sound treatments for these populations.
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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 | 0.013 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".