Evaluation of the Referral Process and Patterns to a Canadian Specialized Eating Disorders Treatment Program.
Bibliographic record
Abstract
OBJECTIVE: To describe the referral process and patterns to the Calgary Eating Disorders Program (CEDP). METHOD: A retrospective chart review for the study period of May 2014 to May 2016 was completed and a descriptive evaluation of the referral process was outlined. RESULTS: The results summarize the steps in the referral process from initiation of referral to booking an assessment. The CEDP received 918 referrals during the study period, yet 60% did not materialize into a patient assessment. Regardless of age, the two most common reasons were patients declined treatment and did not meet program criteria. Physicians who refer to the CEDP are mostly female, family physician specialty and from Calgary. Patients referred to the CEDP are predominantly females, have an average age of 25 years and are mainly referred for 'eating disorder symptoms-diagnosis unclear', regardless of age. The majority of patients are not severely ill at the time of referral. More than 50% of patients have psychiatric comorbidities, with depression, anxiety and substance abuse being the most common. The average wait times to the CEDP are 12 weeks. CONCLUSIONS: This is the first study in Canada to assess referral patterns to a specialized eating disorders program. Results from this study have elucidated the reasons for referral fall-through and highlighted areas of improvement in the referral process. Understanding referral trends is a necessary foundation to advance our knowledge of the factors that contribute to referrals materializing into assessments and ultimately optimizing patient care.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".