Impact of eating disorders on paid or unpaid work participation and performance: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
BACKGROUND: Eating disorders (ED) can reduce quality of life by limiting participation and performance in social and occupational roles, including paid or unpaid work. The association between ED pathologies and work participation and performance must be well understood to strengthen vocational rehabilitation programmes and prevent occupational disruptions in the ED population. The aims of this study are: (1) to examine the degree of association between ED pathologies and work participation and performance in 15-year-olds and older; (2) to highlight the specific ED symptoms that are most correlated with changes in work performance and participation; (3) to compile the most common metrics and assessments used to measure work participation and performance with ED. METHODS: Medline, Embase, CINAHL, Web of Science, PsycINFO, and Cochrane Library will be searched for observational and experimental studies that meet the following criteria: (1) a clinical sample of typical or atypical ED; (2) paid or unpaid employment or training; (3) an association between ED pathologies and work participation or performance. Unpublished data will also be examined. Title and abstract, and full-text screening will be conducted in duplicate. Risk of bias and quality of evidence assessments will be completed. A random-effect meta-analysis will be performed. DISCUSSION: This synthesis can clarify knowledge and gaps around the impact of ED on work functioning, thereby allowing better evaluation, improvements and development of current workplace assessments, interventions, and policies. TRIAL REGISTRATION: The registration number for this systematic review on PROSPERO is CRD42021255055.
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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 | 0.058 | 0.077 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.029 | 0.030 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.005 |
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".