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Record W2061834544 · doi:10.1097/nmd.0b013e3181567fdd

Child Abuse and Health-Related Quality of Life in Adulthood

2007· article· en· W2061834544 on OpenAlexaff
Tracie O. Afifi, Murray W. Enns, Brian J. Cox, Ron de Graaf, Margreet ten Have, Jitender Sareen

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2007
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhysical abuseNeglectPsychological abuseChild abuseMental healthPsychiatrySexual abuseMedicineClinical psychologyQuality of life (healthcare)PopulationIntervention (counseling)PsychologyPoison controlSuicide preventionEnvironmental health

Abstract

fetched live from OpenAlex

Past research has indicated that child abuse is related to mental and physical health conditions and that mental and physical health conditions are related to decreased health-related quality of life (HRQOL). However, little is known about the independent relationship between child abuse and HRQOL. For the current analysis, data were from the nationally representative Netherlands Mental Health Survey and Incidence Study. Multiple linear regression analyses tested the relationships between child abuse and current HRQOL (SF-36) after adjusting for the effects of sociodemographic variables and numerous psychiatric disorders and physical health conditions. Neglect, psychological abuse, physical abuse, severe sexual abuse, and number of types of child abuse experienced were associated with reduced mental HRQOL. Psychological abuse, physical abuse, and number of types of child abuse experienced were associated with reduced physical HRQOL. Child abuse is an important determinant of HRQOL. The ability to successfully reduce the occurrence of child abuse or provide early intervention after child abuse occurs may help to improve HRQOL in the general population.

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)0.006
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.333
Teacher spread0.302 · 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

Citations121
Published2007
Admission routes1
Has abstractyes

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