Partnerships for improving dementia care in primary care: Extending access to primary care‐based memory clinics in Ontario, Canada
Bibliographic record
Abstract
In Ontario, Canada, the Primary Care Collaborative Memory Clinic (PCCMC) model of dementia care provides a team-based assessment and management service that has demonstrated increased capacity for dementia care at the primary care level. PCCMCs are established following completion of a multi-faceted memory clinic training programme. Evidence of the success of this care model has been demonstrated primarily in practice settings with integrated interprofessional healthcare providers (HCPs). Desire to implement PCCMCs in less-resourced family practice settings without integrated interprofessional HCPs has resulted in partnerships with community agencies and services to create the multifaceted teams needed for this care model. The purpose of this study was to describe the key lessons learned in the development and implementation of 18 PCCMCs in primary care practice models without integrated interprofessional HCPs. Mixed methods included tracking of clinic referrals, pre- (N = 122) and post- (N = 71) training surveys to assess practice changes and factors facilitating and challenging clinic implementation. Interviews were conducted with 40 team members to identify key lessons learned. Key enablers were access to training, organisational/ management and care provider support, availability of infrastructure supports and clinic coordination. Data were collected between January 2012 and January 2017. PCCMCs were challenged by a lack of sustainable funding, inadequate infrastructure support, competing priorities, maintaining adequate communication among team members, and coordinating multiple schedules. Suggestions to support longer term sustainability were identified, many addressing identified challenges such as securing sustainable funding, and ensuring partners understand the importance of their role and succession planning. This study demonstrated that by establishing community partnerships and leveraging existing community resources, the PCCMC model is generalisable to multiple family practice settings including those without integrated interprofessional staff. Lessons learned can inform the development of interventions for complex chronic conditions requiring interprofessional support in primary care.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".