[P‐138]: Cultural profiles of a memory clinic ‐ Are we doing enough?
Bibliographic record
Abstract
With the aging of Canada's communities, geriatric mental health services need to examine issues pertaining to accessibility and appropriateness in the context of its multicultural society. The aim was to review the multilingual multicultural memory clinic operation process and organizational characteristics in order to develop strategies to address issues of access in diverse communities. The study also hoped to identify the socio-demographics trends in one of Canada's aging communities - metropolitan Toronto and provide evidence for a multi specialty and multidisciplinary approach to the diagnosis and management of patients with possible dementia across ethno-cultural divides. Sociodemographic databases and the Census data were reviewed for comparison of catchment area representation of different ethnic groups. A comparative analysis of referrals to the memory clinic where done. The 2001 census figures confirm that Greater Toronto is the most multicultural region in Canada. 15% of the total Canadian population belong to a visible minority and of these 10% are over the age of 65. Close to half the residents speak a mother tongue other than English, which is an increase of 17.8 per cent since 1996 (Statistics Canada 2002 .The clinic provided brief dementia screening for English and non-English speaking patients. A multidisciplinary team provided assessments and diagnostic services in nine different languages (English, French, Italian, Portuguese, Spanish, Greek, Mandarin, Cantonese and Hindi) to referred patients from the Province of Ontario. A review of referrals made to the clinic showed dispersion amongst language and cultural profiles. Culture is increasingly recognized as a crucial variable in the delivery of health care services. Diagnosis and treatment planning and implementation require special skills and sensitivities when the health care practitioner and the patient are from different cultures. Low attendance by certain ethnic groups may reflect barriers faced by this population other than language with respect to accessing care. There is a need to increase ethnically relevant information, and measures to improve the acceptability and accessibility of services. Strategies adopted in increasing awareness in the ethnic communities may help improve issues of access and help mitigate associated stigma in these communities.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".