Stigma of dementia during the COVID-19 pandemic: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Dementia-related stigma reduces the quality of life of people living with dementia and their care partners. However, there is a dearth of literature synthesising knowledge on stigma of dementia during the COVID-19 pandemic. This scoping review protocol outlines a methodology that will be used to understand the impact of stigma on people living with dementia during the pandemic. Addressing dementia-related stigma is critical to promoting timely dementia diagnoses and enhancing the quality of life for people living with dementia and their care partners. METHODS AND ANALYSIS: This review will follow the Arksey and O'Malley methodological framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist. The review will focus on English-language, peer-reviewed literature published between 13 January 2020 and 30 June 2023. Stigma will be broadly defined according to pre-established components (stereotypes, prejudice and discrimination). We will search six databases including CINAHL, EMBASE, Google Scholar, Medline, PsycINFO and Web of Science. We will also hand-search the reference lists of relevant articles to identify additional manuscripts. Two reviewers will develop the data extraction table, as well as independently conduct the data screening. Any disagreements will be resolved through open discussion between the two researchers, and if necessary, by consulting the full team to achieve consensus. Data synthesis will be conducted using an inductive thematic analysis approach. ETHICS AND DISSEMINATION: This review will be the first to explore the impact of dementia-related stigma during the COVID-19 pandemic. An advisory panel including a person living with dementia and a care partner will be consulted to inform our review's findings and support the data dissemination process. The results of this scoping review will be shared and disseminated through publication in a peer-reviewed journal, presentations at academic conferences, a community workshop and webinars with various stakeholders.
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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 | 0.152 | 0.127 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.095 | 0.025 |
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".