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Record W3198225486 · doi:10.3389/fpsyt.2021.709516

An Examination of Correlates of Quality of Life in Children and Youth With Mental Health Issues

2021· article· en· W3198225486 on OpenAlexaff
Angela Celebre, Shannon L. Stewart, Laura Theall, Natalia Lapshina

Bibliographic record

VenueFrontiers in Psychiatry · 2021
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsChild and Family Research InstituteWestern University
Fundersnot available
KeywordsMental healthQuality of life (healthcare)AutonomyAnhedoniaMedicineClinical psychologyPopulationPsychiatryPsychologyGerontologyEnvironmental health

Abstract

fetched live from OpenAlex

Quality of life (QoL) is significantly lower in children with mental health issues compared to those who are typically developing or have physical health problems. However, little research has examined factors associated with QoL in this particularly vulnerable population. To address this limitation, 347 clinically referred children and adolescents were assessed using the interRAI Child and Youth Mental Health (ChYMH) Assessment and Self-reported Quality of Life- Child and Youth Mental Health (QoL-ChYMH). Hierarchical multiple linear regression analyses were conducted to examine QoL at the domain-specific level. Children and adolescents who experienced heightened anhedonia and depressive symptoms reported lower social QoL (e.g., family, friends and activities; p = 0.024, 0.046, respectively). Additionally, children and youth who experienced heightened depressive symptoms reported lower QoL at the individual level (e.g., autonomy, health; p = 0.000), and level of basic needs (e.g., food, safety; p = 0.013). In contrast, no mental state indicators were associated with QoL related to services (e.g., school, treatment). Due to the paucity of research examining predictors of QoL in children and youth with mental health challenges, this study contributes to the field in assisting service providers with care planning and further providing implications for practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.384
Teacher spread0.356 · 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 teacher head, 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

Citations41
Published2021
Admission routes1
Has abstractyes

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