Long-Term Care Resident Health and Quality of Care During the COVID-19 Pandemic: A Synthesis Analysis of Canadian Institute for Health Information Data Tables
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Bibliographic record
Abstract
Objective: Long-term care (LTC) homes ("nursing homes") were challenged during the first year of the COVID-19 pandemic in Canada. The objective of this study was to measure the impact of the COVID-19 pandemic on resident admission and discharge rates, resident health attributes, treatments, and quality of care. Design: Synthesis analysis of "Quick Stats" standardized data table reports published yearly by the Canadian Institute for Health Information. These reports are a pan-Canadian scorecard of LTC services rendered, resident health characteristics, and quality indicator performance. Setting and participants: LTC home residents in Alberta, British Columbia, Manitoba, and Ontario, Canada that were assessed with the interRAI Minimum Data Set 2.0 comprehensive health assessment in fiscal years 2018/2019, 2019/2020 (pre-pandemic period), and 2020/2021 (pandemic period). Methods: Risk ratio statistics were calculated to compare admission and discharge rates, validated interRAI clinical summary scale scores, medication, therapy and treatment provision, and seventeen risk-adjusted quality indicator rates from the pandemic period relative to prior fiscal years. Results: Risk of dying in the LTC home was greater in all provinces (risk ratio [RR] range 1.06-1.18) during the pandemic. Quality of care worsened substantially on 6 of 17 quality indicators in British Columbia and Ontario, and 2 quality indicators in Manitoba and Alberta. The only quality indicator where performance worsened during the pandemic in all provinces was the percentage of residents that received antipsychotic medications without a diagnosis of psychosis (RR range 1.01-1.09). Conclusions and implications: The COVID-19 pandemic has unveiled numerous areas to strengthen LTC and ensure that resident's physical, social, and psychological needs are addressed during public health emergencies. Except an increase in potentially inappropriate antipsychotic use, this provincial-level analysis indicates that most aspects of resident care were maintained during the first year of the COVID-19 pandemic.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it