Mental Health Service Use Among Children with Chronic Physical Illness.
Bibliographic record
Abstract
Background: Children with chronic physical illness are more likely to experience adverse mental health; however, the extent of their mental health service use is relatively unknown. Objectives: This study described the patterns of mental health service use over 24 months and identified sociodemographic and health-related factors associated with patterns of use among children with chronic physical illness. Methods: Data come from a longitudinal study of 263 children ages 2 to 16 years with chronic physical illness. Measures of mental health service use were parent-reported. Results: Approximately one quarter of parents reported that their child had some form of contact with a health professional for their mental health. Latent class analyses at baseline and 24 months determined a two-class model with one class reporting any service contact for their mental health (11.4-16.4%) while the second class reported no service use (88.6-83.7%). Child age (OR = 1.30 [1.15, 1.46]), comorbid mental health conditions (OR = 5.58 [2.19, 14.18]), elevated disability (OR = 1.09 [1.02, 1.17]), and higher parental educational attainment (OR = 3.12 [1.56, 6.26]) were associated with any service use class. Conclusion: Mental health service needs are common in children with chronic physical illness, and use of mental health services is related to sociodemographic and health-related factors. These results underscore the need to integrate physical and mental health services in this population. Future research among more diverse samples using data linkages to health records should be undertaken to mitigate the potential limitations of parent-reported service use.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".