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Record W7045437436

The Association Between Childhood Poverty and Adversity and the Likelihood of Experiencing Co-occurring Psychiatric and Substance Use Disorders

2017· dissertation· en· W7045437436 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2017
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversity of Waterloo
KeywordsMultinomial logistic regressionPovertyAssociation (psychology)Substance abuseLogistic regressionBivariate analysisMental healthPublic healthAlcohol use disorder
DOInot available

Abstract

fetched live from OpenAlex

Co-occurring disorder (COD) refers to concurrent psychiatric and substance use disorders (SUD). Compared to those with a single disorder, individuals with COD often require more complex treatment, have poorer health outcomes, and incur higher treatment costs. Researchers have extensively studied both the high lifetime prevalence and age of onset for psychiatric disorders and SUD independently, but little is known about the social antecedents of COD, especially how these antecedents vary by race/ethnicity and gender. I expect the antecedents do not behave universally, though they are currently treated that way. Guided by the Stress Process Model, the Theory of Fundamental Causes, and the Life Course Perspective, this dissertation aims to better understand the role of childhood poverty and childhood adversity in the occurrence of COD for males and females, and for different racial/ethnic groups. This dissertation employs a secondary analysis of existing community-based survey data recorded in the National Epidemiologic Survey of Alcohol and Related Conditions III. Using multinomial logistic regression with a four-category variable for disorder (categories: COD, SUD only, psychiatric disorder only, no disorder), on a bivariate level, childhood poverty is associated with COD, however, with the addition of all other covariates there is no longer an association between poverty and COD. Childhood adversities are strongly associated with COD, net of other factors, in all of the models estimated. There are clear race/ethnicity differences in prevalence of disorder when COD is studied in the whole population. For COD relative to no disorder, Blacks, Asian Americans, and Hispanics, are all approximately half as likely as Whites to have COD, net of other factors. There are no conditional race/ethnicity relationships for COD. There are, however, gender differences in both disorder prevalence and the associations between childhood poverty and COD as well as childhood adversity and COD. Childhood poverty is associated with COD in opposite directions for males and females: for males it increases the relative risk ratio of COD compared to SUD, and for females it decreases the relative risk for this same comparison. This study found no moderation of the childhood poverty and COD relationship by number of adversities in the regressions conducted.Conducting a survival analysis with only respondents who have at least one disorder indicates that having psychiatric disorder compared to having SUD is associated with a 36% increase in the hazard ratio of subsequently developing COD overall. The significant conditional relationship between disorder sequence and gender shows that hazard of co-occurrence with a psychiatric disorder is higher for males than females. On the contrary, the hazard of co-occurrence when one has SUD is higher for females than it is for males.This research has clear public health relevance: above and beyond the genetic risk incurred by having a parent with a disorder, experiencing adverse events in childhood is associated with COD. Efforts to help children and adolescents ameliorate the adversity they are exposed to are important and may be able to diminish the risk of COD associated with harmful early experiences.

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)0.010
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.006
GPT teacher head0.223
Teacher spread0.216 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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