Describing of issues within long-term care during the covid-19 pandemic: inappropriate antipsychotic use in persons with dementia and future strategies to improve pandemic protocols
Bibliographic record
Abstract
During the COVID-19 pandemic, long-term care (LTC) homes in Ontario were left severely underprepared, and many residents, specifically those with behavioral and psychological symptoms of dementia (BPSD), were contained and sedated using antipsychotics as chemical restraints. LTC homes were understaffed, crowded, did not have proper infection control protocols and overall lacked funding: all of which left them underprepared when faced with the COVID-19 pandemic. These issues became massive problems that led to many resident deaths during COVID-19. Though recommendations were given to help improve the above weaknesses in LTC after the severe acute respiratory syndrome (SARS) outbreak of 2002-2004, they were not applied to the LTC sector. This paper will recommend future strategies for Ontario's LTC by analyzing past recommendations and changes made in other countries that saw fewer resident deaths during the COVID-19 pandemic. Specifically, implementing quality improvement (QI) projects to improve weaknesses and test changes on a small scale to measure improvements, and implement policy changes to staffing, crowding, funding, training, and types of therapies given to those with dementia in LTC homes to better pandemic preparedness and decrease the use of antipsychotics as chemical restraints.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".