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Record W2105730847 · doi:10.1300/j013v41n04_02

The Relationship Between Childhood Adverse Experiences and Disability Due to Physical Health Problems in a Community Sample of Women

2005· article· en· W2105730847 on OpenAlexaffabout
Lil Tonmyr, Ellen Jamieson, Leslie S. Mery, Harriet L. MacMillan

Bibliographic record

VenueWomen & Health · 2005
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsHamilton Health SciencesPublic Health Agency of CanadaMcMaster UniversityCarleton University
Fundersnot available
KeywordsPhysical abusePsychiatrySexual abusePovertyPhysical disabilitySubstance abuseMental healthLogistic regressionPhysical healthMedicinePsychologyClinical psychologyGerontologyPoison controlSuicide preventionEnvironmental health

Abstract

fetched live from OpenAlex

The goal of this study was to examine the association of physical and sexual abuse in childhood, poverty, parental substance abuse problems and parental psychiatric problems with disability due to physical health problems in a community sample of women. We included 4,243 women aged 15-64 years from the Ontario Mental Health Supplement in the analysis. The associations were tested by multiple logistic regression. Ten percent of women had a disability due to physical health problems. Among women with a disability, approximately 40% had been abused while growing up. After controlling for income and age, disability showed the strongest association with childhood physical abuse, parental education less than high school and parental psychiatric disorder. The association with child sexual abuse was not significant. Given the high correlation between abuse and disability due to physical health problems, it is important to investigate approaches to identify women who are at increased risk of subsequent impairment.

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)0.003
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0020.000

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 head0.050
GPT teacher head0.359
Teacher spread0.309 · 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

Citations15
Published2005
Admission routes2
Has abstractyes

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