The financial and social impacts of the COVID-19 pandemic on youth with eating disorders, their families, clinicians and the mental health system: a mixed methods cost analysis
Bibliographic record
Abstract
BACKGROUND: The onset of the COVID-19 pandemic has had an adverse impact on children, youth, and families with eating disorders (EDs). The COVID-19 pandemic exacerbated pre-existing personal and financial costs to youth, caregivers, and health professionals accessing or delivering ED services. The objectives of this mixed methods study were to (1) understand the indirect, direct medical and non-medical costs reported by youth, caregivers, and clinicians; (2) understand how the COVID-19 pandemic may have impacted these costs, and (3) explore implications of these costs with regards to barriers and resources to inform future decisions for the ED system of care. METHODS: Youth (aged 16-25 years) with lived/living experience, primary caregivers, clinicians, and decision-makers were recruited with support from various partners across Canada to complete group specific surveys. A total of 117 participants responded to the survey. From those respondents, 21 individuals volunteered to further participate in either a discussion group or individual interview to provide additional insights on costs. RESULTS: Youth and primary caregivers reported costs relating to private services, transportation and impacts of not attending school or work. Additionally, primary caregivers reported the top direct medical cost being special food or nutritional supplements (82.8%). In discussion groups, youth and caregivers elaborated further on the challenges with long waitlists and cancelled services, impact on siblings and effect on family dynamics. Clinicians and decision-makers reported increased work expectations (64.3%) and fear/isolation due to COVID-19 in the workplace (58.9%). Through discussion groups, clinicians expanded further on the toll these expectations took on their personal life. Approximately 1 in 3 health professionals reported contemplating leaving their position in 1-2 years, with greater than 60% of this group stating this is directly related to working during the pandemic. CONCLUSIONS: Findings demonstrate the need for increased support for youth and caregivers when accessing ED services both during crisis and non-crisis times. Additionally, attention must be given to acknowledging the experience of health professionals to support better retention and resource management as they continue to navigate challenges in the health care system.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".