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2023· article· en· W6885945314 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusMental healthCohortHealth careCohort studyBiomedicineRetrospective cohort studyGestational diabetesPregnancy

Abstract

fetched live from OpenAlex

<div><p>Introduction</p><p>Pregnant people have a higher risk of severe COVID-19 disease. They have been disproportionately impacted by COVID-19 infection control policies, which exacerbated conditions resulting in intimate partner violence, healthcare access, and mental health distress. This project examines the impact of accumulated individual health decisions and describes how perinatal care and health outcomes changed during the COVID-19 pandemic.</p><p>Objectives</p><p></p><p></p><p><i>Quantitative strand</i>: Describe differences between 2019, 2021, and 2022 birth groups related to maternal vaccination, perinatal care, and mental health care. Examine the differential impacts on racialized and low-income pregnant people.</p><p></p><p></p><p><i>Qualitative strand</i>: Understand how pregnant people’s perceptions of COVID-19 risk influenced their decision-making about vaccination, perinatal care, social support, and mental health.</p><p></p><p></p><p>Methods and analysis</p><p>This is a Canadian convergent parallel mixed-methods study. The <u>quantitative strand</u> uses a retrospective cohort design to assess birth group differences in rates of Tdap and COVID-19 vaccination, gestational diabetes screening, length of post-partum hospital stay, and onset of depression, anxiety, and adjustment disorder, using administrative data from ICES, formerly the Institute for Clinical Evaluative Sciences (Ontario) and PopulationData BC (PopData) (British Columbia). Differences by socioeconomic and ethnocultural status will also be examined. The <u>qualitative strand</u> employs qualitative description to interview people who gave birth between May 2020- December 2021 about their COVID-19 risk perception and health decision-making process. Data integration will occur during design and interpretation.</p><p>Ethics and dissemination</p><p>This study received ethical approval from McMaster University and the University of British Columbia. Findings will be disseminated via manuscripts, presentations, and patient-facing infographics.</p><p>Trial registration</p><p><b>Registration:</b> Clinicaltrials.gov registration number: <a href="https://clinicaltrials.gov/ct2/show/NCT05663762" target="_blank">NCT05663762</a>.</p></div>

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)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2380.012

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 head0.109
GPT teacher head0.381
Teacher spread0.272 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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