Quality of care for community-dwelling older adults living with dementia in BC: an interrupted time-series analysis to examine the effect of the COVID-19 pandemic
Bibliographic record
Abstract
ObjectiveQuality care for older adults living with dementia (OALwD) is a priority in Canada, nationally, and British Columbia (BC), provincially. The COVID-19 pandemic likely changed care quality, but this has not been assessed. We are examining the effect of pandemic-related policy changes on quality of care for community-dwelling OALwD in BC. ApproachThis population-based retrospective cohort study uses linked administrative data to examine trends in population-level indicators of quality care from 2016-2023. We will compare effects among community-dwelling BC residents aged 65+ with dementia and those without dementia. Indicators including hospitalization rate and medication use were selected, based on the Institute of Medicine’s six dimensions of quality (e.g., effectiveness, safety). Interrupted time series (ITS) analysis will examine changes between “pre-pandemic” and “in-pandemic” periods. Equity will be assessed through stratification by sociodemographic variables. Anticipated ResultsPercent change in the level and slope of each indicator’s trend will be reported. Single ITS will identify the effect of the pandemic on each outcome in the dementia group only. Controlled ITS will identify whether changes differ from those observed in older adults without dementia. Together, observed changes in the indicators and differences across equity factors will inform an interpretation of the overall effect of the pandemic on quality of care for community-dwelling OALwD in BC. ImplicationsWe will determine whether quality of care changed for older adults because of the pandemic and whether changes differed for OALwD. This knowledge will inform practice and policy for delivery of high quality, proactive dementia care.
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 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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".