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Record W7141771535

Mental Health Service Use Among Children with Chronic Physical Illness.

2025· article· en· W7141771535 on OpenAlexaff
Lauren Gosse, Chloe Bedard, Scott T. Leatherdale, Christopher M. Perlman, Mark A. Ferro

Bibliographic record

VenuePubMed · 2025
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMental healthMental health servicePhysical healthService (business)Health servicesChronic disease
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.277
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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