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Record W4400990266 · doi:10.1186/s12888-024-05971-1

The correlations on psychopathology in children self-rating, psychopathology in children as related by their parents and psychopathology in parents self-rating in a Kenyan school setting: towards an inclusive family-centered approach

2024· article· en· W4400990266 on OpenAlexfundno aff
David M. Ndetei, Victoria Mutiso, Pascalyne Nyamai, Christine Musyimi

Bibliographic record

VenueBMC Psychiatry · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsPsychopathologyPsychologyClinical psychologyChild psychopathologyRating scaleKenyaDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Several studies have reported on the association between parental and childhood psychopathologies. Despite this, little is known about the psychopathologies between parents and children in a non-clinical population. We present such a study, the first in a Kenyan setting in an attempt to fill this gap. The objective of this study was to determine the association between self-rating psychopathology in children, parent-rating psychopathology in their children and self-rating psychopathology in parents in a non-clinical population of children attending schools in Kenya. We identified 113 participants, comprising children and their parents in 10 randomly sampled primary schools in South East Kenya. The children completed the Youth Self-Report (YSR) scale and parents completed the Child Behavior Check List (CBCL) on their children and the Adult Self-Reports (ASR) on themselves. These instruments are part of the Achenbach System of Empirically Based Assessment (ASEBA), developed in the USA for a comprehensive approach to assessing adaptation and maladaptive behavior in children and adolescents. There was back and forth translation of the instruments from English to Swahili and the local dialect, Kamba. Every revision of the English translation was sent to the instrument author who sent back comments until the revised version was in sync with the version developed by the author. We used the ASEBA in-built algorithm for scoring to determine cut-off points for problematic and non-problematic behavior. Correlations, linear regression and independent sample t-test were used to explore these associations. The mean age of the children was 12.7. While there was no significant association between child problems as measured by YSR (self-reported) and parent problems as measured by ASR and CBCL in the overall correlations, there was a significant association when examining specific groups (clinical range vs. non-clinical). Moreover, significant association existed between total problems on YSR and ASR internalizing problems (t=-2.3,p = 0.023), with clinical range having a higher mean than the normal range. In addition, a significant relationship (p < 0.05) was found between psychopathology in children as reported by both parents (CBCL) and psychopathology in parents as self-reported (ASR).Mothers were more likely to report lower syndrome scores of their children as compared to fathers. Our findings indicate discrepancies between children self-rating and parent ratings, suggesting that one cannot manage psychopathology in children without reference to psychopathology in their parents. We suggest broad-based psycho-education to include children and parents to enhance shared awareness of psychopathology and uptake of treatment.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.009
GPT teacher head0.276
Teacher spread0.268 · 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
Published2024
Admission routes1
Has abstractyes

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