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Record W4403679597 · doi:10.1093/pch/pxae067.006

06 Eating disorder toolkit 2023: Standards of practice in the primary care setting

2024· article· en· W4403679597 on OpenAlexaboutno aff
Jennifer Mooney

Bibliographic record

VenuePaediatrics & Child Health · 2024
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsPrimary carePsychologyMedicineMedical educationFamily medicine

Abstract

fetched live from OpenAlex

Abstract Background Throughout the COVID 19 pandemic, physicians across Canada began to see a 'shadow pandemic' unfold as new diagnosis of eating disorders and hospital admissions for patients with medically unstable eating disorders increased exponentially. As these rates remain high, specialists stressed the importance of the role primary care physicians play in the early recognition, assessment, and diagnosis of eating disorders as a way to improve prognosis. Further, the high volume of patients has resulted in the need to manage patients in the community or engage in collaborative care between the eating disorder specialist and primary care physician. This Eating Disorder Toolkit was created as a knowledge mobilization resource to provide primary care and community providers with Standards of Practice and improve care for eating disorder patients. Objectives 1. Provide an updated and easily readable evidence-based reference tool for PCPs 2. Clarify PCPs role in working with, monitoring, and treating EDs in a shared care model 3. Strive for early diagnosis and connection to care for improved prognosis and decreased morbidity 4. Highlight unique considerations for specific populations 5. Align with the upcoming Eating Disorders Pathway project Design/Methods The first edition of the ED Toolkit was reviewed and based on feedback sought and provided by key partners including PCPs already using the Toolkit, areas of improvement were identified. These included formatting to improve accessibility in the primary care setting, updating the guidelines, and including unique considerations for specific populations. A literature review of current guidelines and practices internationally was completed. Experienced paediatric and adult ED specialists reviewed the material to ensure it was appropriate for clinical practice. Key partners contacted for feedback and collaboration included the Provincial ED Steering Committee, Provincial ED Pathway Project Committee, UBC Continuing Professional Development, and Primary Care physicians. This ensured the resource was meeting PCPs identified needs and was usable in the primary care setting. Edits were made to reflect the feedback received. Finally, the resource was reviewed to ensure alignment with the upcoming Provincial ED Pathway Project. Results The Eating Disorder Toolkit 2023: Standards of Practice in the Primary Care Setting is now available and has been distributed widely across British Columbia. In addition, Kelty Mental Health and Compass, leading resource hubs in the province, host the live document on their sites. Finally, the toolkit is also provided to any health providers upon request to their regional ED clinic. While we have not formally studied the outcome of this resources, anecdotal reports via email and verbal feedback from nurse practitioners, paediatricians, and ED specialists across the province has been positive. Conclusion Eating disorders are complex illnesses associated with significant medical and psychiatric complications. Early recognition and management improves prognosis. Community providers play a crucial role in recognizing and managing these patients, however many do not have expertise in this area. It is hoped that this resource will support PCPs in managing ED patients in British Columbia and there may be a potential scope for use across Canada with adaptations to local guidelines and resources.

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 imitation

Not 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.

metaresearch head score (Codex)0.114
metaresearch head score (Gemma)0.228
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.114
Threshold uncertainty score0.605

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1140.228
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0030.002
Scholarly communication0.0090.006
Open science0.0060.013
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0230.016

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.

Opus teacher head0.010
GPT teacher head0.342
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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