One-Year Report of COVID-19 Impact on Geriatric Patients: a Bio-Psycho-Social Approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".