Mental Health Service Use Among Children with Chronic Physical Illness
Bibliographic record
Abstract
Background: Children with chronic physical illness (e.g., diabetes, epilepsy) are significantly more likely to experience adverse mental health. While mental health service use (MHSU) amongst Canadian children has increased dramatically in the past two decades, the extent of service use among those with co-occurring physical and mental illness (i.e., multimorbidity) is relatively unknown. The cross-sectional design of previous research limits our understanding of how MHSU for children with chronic physical illness changes over time and the factors that predict differences in patterns of use. This study used longitudinal data to overcome limitations of previous work to better understand MHSU among children with chronic physical illness. \n \nObjectives: This study described the frequency and patterns of MHSU among children and youth (herein children) with chronic physical illness, examined how patterns of MHSU for these children change over a 24-month period, and identified sociodemographic and health-related factors associated with patterns of MHSU among children with chronic physical illness. \n \nMethods: Data come from a sample of 263 children aged 2-16 years who were diagnosed with a chronic physical illness from McMaster Children’s Hospital. Univariate statistics described the mental health services used by children with multimorbidity. Latent class analysis was used to identify patterns of use (e.g., primarily hospital-based vs. community-based). Multinomial regression was used to model baseline sociodemographic and health factors associated with different patterns of MHSU. \n \nResults: Across all timepoints, approximately one quarter of parents reported that their child had some form of contact with a health professional for their mental health (24.7%). Latent class analysis determined a two-class model with one class reporting any contact for their mental health (11.4% at baseline; 16.4% at 24 months) while the other class reported no service use regarding their mental health (88.6% at baseline; 83.7% at 24 months). In a fully-adjusted model, child age (OR = 1.30 [1.15, 1.46]), presence of one or more mental illness (OR = 5.58 [2.19, 14.18]), level of disability (OR = 1.09 [1.02, 1.17]), and parental educational attainment (OR = 3.12 [1.56, 6.26]) differed significantly between classes. \n \nConclusion: Findings suggest that mental health service needs are pervasive in this group of children given both the proportion as well as the array of combinations of health care providers reported by children and their families. Latent class analysis showed a two-class solution that differed with regards to several sociodemographic and health-related factors between those who did and did not report service use. Future directives are required to parse the complex interactions children and their families must navigate. Larger, more diverse samples should be studied in order to replicate findings, and data linkages to health records should be undertaken in effort 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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".