B.07 Differences between Indigenous and non-Indigenous patients referred to a rural and remote memory clinic
Bibliographic record
Abstract
Background: Jacklin et al. (2013) described a rising incidence and a younger onset of dementia in Albertan First Nations compared to non-First Nations patients. Canadian research is limited in Indigenous patients with dementia, leaving it difficult to understand factors contributing to the differences in incidence and prevalence. Methods: 375 patients (41 Indigenous) was seen at the clinic. The questionnaire given during initial assessments were reviewed and differences between groups (non-Indigenous patients versus Indigenous) were assessed. Results: Compared to the non-Indigenous patient, Indigenous patients were younger (p=0.007), were more likely to be female (p=0.033) and had less education (p=0.055). They were less likely to live solely with a partner (p<0.001) and more likely to have a daughter as caregiver (p=0.004). The Indigenous patients were more likely to smoke (p<0.001). Although no differences in diagnosis of mental health disorders were seen (p=0.735), the Indigenous patients scored significantly higher on the CES-D (p<0.0001). Conclusions: This comparison highlights differences potentially affecting the health of Indigenous patients. Acknowledging these differences is critical to individualized patient care. Further research is required to explore how these factors affect dementia disease course and treatment, and how these factors play a role in the differences in incidence and prevalence demonstrated in previous studies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".