Equity in Changes to Dementia Care in the Community during the First Wave of the COVID-19 Pandemic in High Income Countries: A Scoping Review
Bibliographic record
Abstract
During COVID-19, emergency measures, such as physical distancing and program restrictions, have reduced community-based supports for PLWD and their caregivers. Consequently, reductions in dementia services and resources have contributed to existing health inequities in this population. Academic databases were searched in July 2020. Grey literature was retrieved using the CADTH Grey Matters tool. Articles from 2000 to 2020 in English and from high-income countries were included. Literature that discussed any changes to community support and services for PLWD and/or their caregivers during any infectious respiratory outbreak was included. Findings were extracted using a template adapted from the Health Equity Impact Assessment (HEIA) tool. A total of 15 articles were identified; all focused on the COVID-19 pandemic. Evidence was primarily based on expert opinion, with only three primary research studies meeting inclusion criteria. Most alterations to dementia services described switching to telehealth platforms. There was limited information on social determinants of health and how these intersected to influence the experience of service changes among different populations. More research is needed to better understand how services for PLWD can continue or be transitioned online during infectious disease outbreaks and address issues of health (in)equities for PLWD and/or their caregivers.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".