Bibliographic record
Abstract
Although primary care in Canada is ideally positioned to offer comprehensive dementia care, typically there has been a reliance on specialists for dementia care, with lengthy wait times for assessment. In Ontario, Primary Care Collaborative Memory Clinics (PCCMCs) have transformed the system of care for persons with dementia by building capacity for dementia assessment and management in primary care a through collaboration between primary care physicians, specialists, interprofessional health care providers, and community agencies. Through a standardized accredited training program, 77 PCCMCs have been established across the province, serving over 1000 primary care practices with patient base of 1,500.000. This presentation will describe the PCCMC care model and its impact on access to care, specialist referrals, and dementia care for patients and caregivers. Referrals and service provision data collected from 32 PCCMCs were tracked for an average of nine months following clinic launch. Data was collected on number of referrals, assessments conducted, and referrals to specialists, and wait time to assessment. Eight months following clinic launch, team members were surveyed to identify benefits of this care model to patients and caregivers. Across the PCCMCs, 1553 patients were referred for assessment, of which 75% had been assessed during the study period. Average wait time for assessment was 1.5 months; 35% of patients were assessed within a month of referral. Fewer than 20% of patients waited more than three months for assessment. Nine percent (N = 104/1166) of patients were referred to specialists for consultation. Surveys were completed by 198 of 363 (54%) of team members. Responses highlighted benefits related to improved and timely access to care close to home, improved quality of care with collaborative team-based care, including greater access to caregiver supports, and improved continuity of care. PCCMCs represent a significant opportunity to build capacity for timely, accessible person-centred dementia care within primary care practice, making more efficient use of limited specialist resources. The provision of comprehensive quality care in primary care can strengthen the system’s ability to meet the needs of persons with dementia, the demand for which will escalate with an aging population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".