Telebehavioral Health for Caregivers of Children With Behavioral Health Needs to Address Caregiver Strain: Cohort Study
Bibliographic record
Abstract
BACKGROUND: Behavioral health conditions among children have worsened over the past decade. Caregivers for children with behavioral health conditions are at risk for two types of caregiver strain: (1) an objective strain, that results directly from the child's condition and (2) subjective strain, that arises from the caregiver's feelings regarding these events. OBJECTIVE: This study aimed to evaluate the impact of a technology-enabled pediatric and family behavioral health service on caregivers' strain among a commercially insured population. We also explore the common symptom clusters of caregiver strain to better understand the caregiver presentation to inform future care planning. METHODS: We examined changes in caregiver strain using the Caregiver Strain Questionnaire-Short Form 7 over the course of their child's web-based behavioral health care between 2021 and 2023 using a pre-post study design. Common caregiver strain symptom clusters were identified using Ward hierarchical agglomerative clustering. RESULTS: The majority of children were White 60.8% (1002/1647), female 53.6% (882/1647), and aged between 5 and 9 years (33.7%, 555/1647). Families fall broadly into 4 groups based on what drives caregiver strain the most, namely those experiencing (1) disrupted family relationships and time interruption, (2) missed work, (3) worried about their child's future and feeling tired and sad, and (4) financial strain. Caregiver strain, which was associated with the child's disease severity (P<.001), decreased significantly in all therapeutic groups. CONCLUSIONS: Web-based family-oriented behavioral health care can improve caregiver strain and reduce family and time disruptions, missed work, and financial strain. Sources of caregiver strain vary and may be overlooked when relying on the conventional scoring of the Caregiver Strain Questionnaire-Short Form 7.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".