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Record W4281877944 · doi:10.5770/cgj.25.553

One-Year Report of COVID-19 Impact on Geriatric Patients: a Bio-Psycho-Social Approach

2022· review· en· W4281877944 on OpenAlexaffvenue
Allen Y. Chang, Kimberly N. Babb

Bibliographic record

VenueCanadian Geriatrics Journal · 2022
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLonelinessMedicinePandemicSocial isolationIsolation (microbiology)Coronavirus disease 2019 (COVID-19)GerontologyGeriatricsSocial distanceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Health carePsychiatryInfectious disease (medical specialty)DiseasePathologyBioinformatics

Abstract

fetched live from OpenAlex

Background: Since December of 2019, coronavirus 19 (COVID-19) has spread rapidly around the world. Our understanding of the infection has grown over the past year, and its impact on older adults is particularly significant. Apart from the direct impacts of COVID-19 infections, it has also led to lockdowns which, in turn, result in isolation and loneliness. Method: We conducted a literature review of publicly available articles of the COVID-19 pandemic impact on the geriatric population between December 2019 and April 2021, a total of 748 articles. Results: The review will be presented with the Bio-Psycho-Social model, covering how the biological, psychological, and sociological aspects of health are intertwined and impact older adults. Early studies have also highlighted the prevalence of post-COVID infection symptoms that typically fall under geriatric medicine care. We highlight the bidirectional impact of isolation and COVID-19 infections on geriatric health, as well as discuss pertinent topics such as vaccine efficacy, long-term sequelae of COVID-19 infections, and ageism. Conclusion: This review seeks to present a one-year report of what is known about COVID-19 and geriatric medicine, as well as provide guidance to practitioners who care for older adults based on the most up-to-date literature.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.132
GPT teacher head0.433
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2022
Admission routes2
Has abstractyes

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