Examining equity in service use across socioeconomic status in people with dementia
Bibliographic record
Abstract
Abstract INTRODUCTION Inequities associated with socioeconomic status (SES) manifest across dementia outcomes; however, research investigating service use across SES in people with dementia is scarce. METHODS We conducted a repeated yearly cohort study of community‐dwelling people in Quebec with incident dementia (2000–2017), using health administrative data held at the Quebec National Institute of Public Health (INSPQ). We described 23 indicators of health service use and mortality across levels of material deprivation, derived from a validated ecological index based on the average income, employment, and education of residential neighborhoods. RESULTS Age‐standardized rates of 15/23 indicators differed across SES. Among 193,834 older people newly diagnosed with dementia, those from most deprived areas had more hospitalizations, emergency department visits, potentially inappropriate medication prescriptions, and higher 1‐year mortality, though they had higher care continuity. Conversely, rates were comparable across groups for the prescription of dementia‐specific medications and primary care visits. DISCUSSION Stark differences across SES in service use by people with dementia may indicate different health needs and/or allude to pervasive health inequities. These results can inform policies to address the needs of people with lower SES, to offer equitable, appropriate, and needs‐based care to all people living with dementia.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".