Evaluating the Impact of Pediatric Digital Mental Health Care on Caregiver Burnout and Absenteeism: Longitudinal Observational Study
Bibliographic record
Abstract
Background: Caregivers of children with mental health challenges are at heightened risk for burnout and absenteeism. This strain affects both their well-being and work performance, contributing to widespread workplace issues. Digital mental health interventions (DMHIs) are increasingly used to support pediatric mental health, but their impact on caregiver outcomes remains underexplored. Objective: This study aimed to explore the associations between caregiver burnout, absenteeism (ie, missing work), comorbid symptoms, and child mental health problems, and to assess whether caregiver burnout and absenteeism improved as their child participated in a pediatric DMHI. Methods: This retrospective study included 6506 caregivers whose children (aged 1-17 years) received care from Bend Health, Inc, a pediatric DMHI providing digital-based therapy and coaching, digital content, and caregiver support. Caregiver burnout, absenteeism, comorbid symptoms, and child mental health symptoms were measured by monthly assessments. Cumulative link models were used to assess the associations of between child symptoms and caregiver outcomes and to assess changes in caregiver outcomes over the course of the DMHI. Analyses of baseline associations included the full sample (n=6506), while analyses of pre-post changes in caregiver outcomes were conducted in caregivers with elevated burnout (n=2121) and absenteeism (n=1327) who had an assessment after starting care. Results: At baseline, 45.96% (2990/6506) of caregivers reported elevated burnout and 28.96% (1884/6506) reported elevated absenteeism. More severe burnout was associated with having a child with elevated symptoms of any type (all P<.01). More severe absenteeism was significantly associated with having a child with elevated symptoms of depression (z=3.33; P<.001), anxiety (z=3.96; P<.001), inattention (z=2.48; P=.013), and hyperactivity (z=2.12; P=.03). Burnout decreased for 68.64% (1456/2121) and absenteeism decreased for 87.26% (1158/ 1327). Greater months in care was associated with less severe caregiver burnout (z=-5.48; P<.001) and absenteeism (z=-6.74; P<.001). Conclusions: DMHIs for children may reduce caregiver burnout and absenteeism. These findings emphasize the value of employers offering pediatric DMHIs as part of employee benefits, potentially enhancing workplace outcomes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".