Mitigating the Effects of a Pandemic: Facilitating Improved Nursing Home Care Delivery Through Technology
Bibliographic record
Abstract
The coronavirus disease (COVID-19) pandemic has been particularly challenging for nursing home staff and residents. Centers for Medicare & Medicaid Services regulation waivers are burdening staff and affecting how care is delivered. Residents are experiencing social isolation, which can result in physical and behavioral health issues, particularly for persons with dementia. These challenges can be addressed in part through technology adaptations. Full integration of electronic health record systems can improve workflow and care quality. Telehealth can improve access to outside providers, provide remote monitoring, and improve social connectedness. Electronic and audiovisual programs can be used for end-of-life planning and information sharing between nursing home staff and families. Online learning systems and other online resources provide flexible options for staff education and training. Investing in and adapting technology can help mitigate workforce stress and improve the quality of nursing home care during and after the COVID-19 crisis.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".