Eating disorders in primary care: A survey of provider perspectives
Bibliographic record
Abstract
Objective: Eating disorders are complex illnesses resulting in impaired psychological and physiological functioning which can become life-threatening(1). These disorders commonly develop in adolescence or young adulthood, and it is often the primary care office where affected patients first encounter the healthcare system (2). The main goal of this study was to identify whether gaps exist in provider training on how to identify and treat eating disorders, and to assess for barriers to providing treatment in the primary care office. Methods: A brief online survey was distributed to primary care providers (PCPs) in Manitoba to gain an understanding of their confidence and preparedness with identifying and treating eating disorders. The survey also included an open-ended response for participants to provide additional comments or perspectives pertaining to their interactions with eating disorders in their practice. Results: Participants (n = 48) reported treating an average of 4.36 patients with eating disorders over the last 12 months and 4.2% reported utilizing screening tools to assist with identifying eating disorders. Participants further identified a moderate confidence level in evaluating a patient for an eating disorder and low confidence level in their ability to medically manage eating disorders in their practice. Conclusion: This study highlights the current education and training involving eating disorders in family medicine residents and physician assistants working in primary care as being inadequate. The results also suggest PCPs find navigating the current system in Manitoba to access specialized eating disorder treatment difficult and inefficient. There remains a need for increased treatment access. With increased training and resources, the primary care office could assist in filling part of this need.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".