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Record W2107356670 · doi:10.1177/0886260507306484

Child Abuse and Chronic Pain in a Community Survey of Women

2007· article· en· W2107356670 on OpenAlexaff
Christine A. Walsh, Ellen Jamieson, Harriet L. MacMillan, Michael Boyle

Bibliographic record

VenueJournal of Interpersonal Violence · 2007
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsMcMaster UniversityUniversity of Calgary
Fundersnot available
KeywordsChronic painSexual abusePhysical abusePsychiatryClinical psychologyChild abusePsychological abuseAnxietyMental healthMedicinePopulationSubstance abusePoison controlDepression (economics)Suicide preventionPsychologyMedical emergency

Abstract

fetched live from OpenAlex

This study examined the relationship between a self-reported history of child physical and sexual abuse and chronic pain among women (N = 3,381) in a provincewide community sample. Chronic pain was significantly associated with physical abuse, education, and age of the respondents and was unrelated to child sexual abuse alone or in combination with physical abuse, mental disorder (anxiety, depression, or substance abuse), or low income. Number of health problems and mental health disorders did not mediate the relationship between physical abuse and chronic pain. Despite considerable evidence from the clinical literature linking exposure to child maltreatment and chronic pain in adulthood, this may well be the first population-based study to investigate this relationship for child physical and sexual abuse independently. The significant association between childhood history of physical abuse and pain in adulthood calls for a greater awareness of the potential for chronic pain problems associated with this type of maltreatment. Further research is needed to understand the mechanism for this complex relationship.

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)0.002
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 score0.139
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.020
GPT teacher head0.303
Teacher spread0.282 · 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

Citations109
Published2007
Admission routes1
Has abstractyes

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