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Record W2040223465 · doi:10.1080/14623730.2006.9721736

Child Abuse and Disability in an Ontario Community Sample: Does Social Capital Matter?

2006· article· en· W2040223465 on OpenAlexaffabout
Lil Tonmyr, Ellen Jamieson, Leslie S. Mery, Harriet L. MacMillan

Bibliographic record

VenueInternational Journal of Mental Health Promotion · 2006
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsHamilton Health SciencesPublic Health Agency of CanadaMcMaster UniversityCarleton University
Fundersnot available
KeywordsSocial capitalMental healthSample (material)PsychologyPsychiatryEnvironmental healthMedicineSociologySocial science

Abstract

fetched live from OpenAlex

Researchers have established a link between abuse and disability, but most abused individuals do not experience disability. While some survivors are severely harmed by their experiences of abuse, other survivors of similar exposure appear to have no long-term health problems. Can the presence of social capital account for these differences? Cross-sectional data from the Ontario Health Supplement were used to assess the association between child abuse, age, social capital (social connections and disruptions in living circumstances and relationships), cultural capital (education and occupation) and financial capital (money) and disability in a female community sample (n=4238). The results suggest that abuse (physical and sexual) and financial capital are associated with disability, but not social capital.

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)0.004
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 score0.142
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.031
GPT teacher head0.365
Teacher spread0.334 · 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

Citations1
Published2006
Admission routes2
Has abstractyes

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