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Record W4321210592 · doi:10.1136/bmjopen-2022-067449

Assessing the impact of COVID-19 pandemic on the health of residents and the healthcare system in Alberta, Canada: an observational study—The Alberta POST-COVID Follow-up Study

2023· article· en· W4321210592 on OpenAlexafffundabout
Xueyi Chen, Jeffrey A. Bakal, Tara A. Whitten, Barbara Waldie, Chester Ho, Paul Wright, Shahin Hassam, Colleen M. Norris

Bibliographic record

VenueBMJ Open · 2023
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of AlbertaAlberta Health Services
FundersUniversity of AlbertaGovernment of AlbertaWomen and Children's Health Research InstituteChildren's Health Research InstituteAlberta Health Services
KeywordsMedicineCoronavirus disease 2019 (COVID-19)PandemicObservational study2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Health careHealthcare systemEpidemiologyFamily medicineEnvironmental healthVirologyEconomic growthDiseaseOutbreakPathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

INTRODUCTION: Very little is known about how the COVID-19 pandemic has affected the health of residents and the healthcare system in Alberta, Canada. The purpose of this study is to establish an observational study to characterise the health of residents in Alberta, Canada, over time, covering a population that tested negative or positive for COVID-19 during the pandemic. The primary outcome is to characterise 'long COVID-19' and the health status of residents during the COVID-19 pandemic. Secondary outcomes include the estimation of the risk of and risk factors associated with adverse health outcomes and healthcare utilisation and burdens. METHODS AND ANALYSIS: This is a population-level provincial observational study which will follow-up with Alberta residents who underwent testing for COVID-19 and completed surveys adapted from the ISARIC COVID-19 long-term follow-up survey. The survey data will be linked with medical records. Statistical analyses will be carried out to characterise 'long COVID-19' and the health status of residents during the pandemic. The outcomes of this study will inform strategies for primary care and rehabilitation services to prevent chronic consequences; contribute to healthcare management, interventional studies, rehabilitation and health management to reduce overall morbidity and improve long-term outcomes of COVID-19 and the COVID-19 pandemic and potentially guide a self-evaluation of a remote monitoring system to manage individuals' health. ETHICS AND DISSEMINATION: This study was reviewed and approved by the University of Alberta ethics committee (Study ID: Pro00112053 & Pro00113039) on 13 August 2021 and adheres to the Alberta Health Services research information management policy. Study results will be used to manage clinical care, published in peer-reviewed journals and presented at local, national and international conferences. PROTOCOL VERSION: 6 June 2022 EUROQOL ID: 161 015.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.043
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0040.003
Scholarly communication0.0020.001
Open science0.0030.001
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 head0.224
GPT teacher head0.502
Teacher spread0.277 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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