Separate and Unequal: A Time to Reimagine Dementia
Bibliographic record
Abstract
The rapid emergence of COVID-19 has had far-reaching effects across all sectors of health and social care, but none more so than for residential long-term care homes. Mortality rates of older people with dementia in residential long-term care homes have been exponentially higher than the general public. Morbidity rates are also higher in these homes with the effects of government-imposed COVID-19 public health directives (e.g., strict social distancing), which have led most residential long-term care homes to adopt strict 'no visitor' and lockdown policies out of concern for their residents' physical safety. This tragic toll of the COVID-19 pandemic highlights profound stigma-related inequities. Societal assumptions that people living with dementia have no purpose or meaning and perpetuate a deep pernicious fear of, and disregard for, persons with dementia. This has enabled discriminatory practices such as segregation and confinement to residential long-term care settings that are sorely understaffed and lack a supportive, relational, and enriching environment. With a sense of moral urgency to address this crisis, we forged alliances across the globe to form Reimagining Dementia: A Creative Coalition for Justice. We are committed to shifting the culture of dementia care from centralized control, safety, isolation, and punitive interventions to a culture of inclusion, creativity, justice, and respect. Drawing on the emancipatory power of the imagination with the arts (e.g., theatre, improvisation, music), and grounded in authentic partnerships with persons living with dementia, we aim to advance this culture shift through education, advocacy, and innovation at every level of society.
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 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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.040 | 0.093 |
| Scholarly communication | 0.029 | 0.060 |
| Open science | 0.004 | 0.048 |
| Research integrity | 0.018 | 0.048 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".