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Record W2541456247 · doi:10.2147/copd.s107549

Childhood maltreatment as a risk factor for COPD: findings from a population-based survey of Canadian adults

2016· article· en· W2541456247 on OpenAlexafffundabout
Margot Shields, Wendy Hovdestad, Charles Gilbert, Lil Tonmyr

Bibliographic record

VenueInternational Journal of COPD · 2016
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicineCOPDLogistic regressionPsychological interventionMental healthSexual abusePsychiatryPopulationRisk factorProtective factorPoison controlClinical psychologyInjury preventionDemographyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to examine the associations between childhood maltreatment (CM) and COPD in adulthood. METHODS: Data were from 15,902 respondents to the 2012 Canadian Community Health Survey - Mental Health. Multiple logistic regression models were used to examine associations between CM and COPD and the role of smoking and mental and substance use variables as mediators in associations. RESULTS: COPD in adulthood was related to CM, with associations differing by sex. Among females, COPD was related to childhood physical abuse (CPA), childhood sexual abuse, and childhood exposure to intimate partner violence, but in the fully adjusted models, the association with CPA did not persist. Among males, COPD was related to childhood exposure to intimate partner violence and severe and frequent CPA, but these associations did not persist in the fully adjusted models. CONCLUSION: Results from this study establish CM as a risk factor for COPD in adulthood. A large part of the association is attributable to cigarette smoking, particularly for males. These findings underscore the importance of interventions to prevent CM as well as programs to assist victims of CM in dealing with tobacco addiction.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.298
Teacher spread0.276 · 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 teacher head, not a consensus.

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

Citations30
Published2016
Admission routes3
Has abstractyes

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