Fathers' impact on outcomes in the treatment of eating disorders: A scoping review
Bibliographic record
Abstract
OBJECTIVE: Male caregivers' participation in eating disorder (ED) treatment for their affected children is less consistent than female caregivers', with unclear effects. To clarify the impact, this scoping review examined literature on male caregiver involvement in ED treatment, focusing on its impact on fathers, treatment processes, and their affected children. METHODS: A search encompassing English and French peer-reviewed articles from 1990 to 2022 was conducted. Studies distinguishing between mothers and fathers, addressing Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases ED diagnoses, and involving active interventions were included. From 1651 initially identified articles, 251 were retained after abstract and title review, and 45 met all criteria. RESULTS: Documented outcomes indicated fathers' engagement in ED treatment improved their well-being and family functioning, but these gains were not consistently tied to treatment outcomes. Father attendance, improved caregiving skills, and their expectations of treatment correlated with better outcomes for their affected child. CONCLUSIONS: Father involvement in ED treatment remains under-explored. This review emphasises fathers' positive impact while highlighting the need to better understand the link with overall patient outcomes. We call for proactive exploration of how to surmount barriers to fathers' involvement and ensure that paternal contributions are optimised in ED treatment alongside those of female caregivers.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".