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Record W7028411282

Estimation of an individual-level deprivation index in a cohort of HIV-HCV co-infected Canadians and its relationship with health outcomes

2020· dissertation· en· W7028411282 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2020
Typedissertation
Languageen
FieldArts and Humanities
TopicCybernetics and Technology in Society
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchStyrelsen för Internationellt Utvecklingssamarbete
KeywordsEstimationCohortIndex (typography)Cohort study
DOInot available

Abstract

fetched live from OpenAlex

Background: HIV-HCV co-infected individuals are often more deprived than the general population.These factors may lead to disengagement from care.However, deprivation is difficult to measure, and often relies on aggregate data which don't capture individual heterogeneity.We developed an individual-level deprivation index for HIV-HCV co-infected persons that encapsulated social, material, and lifestyle factors. Methods:We estimated an individual-level deprivation index with data from the Canadian Coinfection Cohort, a national prospective cohort study.We used multiple correspondence analyses and exploratory data analyses to select 9 dichotomous variables at baseline visit: income >$1500/month; education >high school; employment; identifying as gay or bisexual; Indigenous status; injection drug use in last 6 months; injection drug use ever; past incarceration, and past psychiatric hospitalization.We fitted an item response theory model with: severity parameters (how likely an item was reported), discriminatory parameters, (how well a variable distinguished index levels), and an individual parameter (the index).We considered two models: a simple one with no provincial variation and a hierarchical model by province.The Widely Applicable Information Criterion was used to compare the models.Finally, we evaluated the association of the index with non-attendance to a second clinic visit (as a measure of disengagement) using logistic regression.Results: We analyzed 1547 complete cases of 1842 enrolled participants; 457 (30%) failed to attend a second visit.The hierarchical model was found to have the best fit.Values of the index were similarly distributed across the provinces.Overall, past incarceration, education, and unemployment had the greatest discriminatory parameters.However, in each province different components of the index were associated with being deprived reflecting local epidemiology.

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)0.002
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.260
Teacher spread0.224 · 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
Published2020
Admission routes1
Has abstractyes

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