Multispecialty Interprofessional Team (MINT) Memory Clinic Overview and Data From Spread Across Canada
Bibliographic record
Abstract
Abstract Background Multispecialty Interprofessional Team (MINT) Memory Clinics build capacity for dementia care within primary care. This presentation will provide an overview of the MINT care model and results of a process evaluation of the implementation of the model in three provinces in Canada using the Research Medical Council framework for evaluating complex interventions. Methods 178 healthcare providers (HCP) were trained to establish 10 MINT clinics across three Canadian provinces. We collected data on clinic referrals and service provision over a nine‐month period to describe fidelity (assessment adherence; management; timely access), dose (number of assessments, follow‐up appointments), and reach (patients served). We conducted individual interviews with 20 key informants (HCP, system leaders) to learn about the contextual factors that affected implementation and clinic impacts. Results Across 10 clinic sites, 521 patients were referred; 368 (71%) completed assessments. All clinics implemented the assessment protocol as fully intended with the exception of two clinics which did not administer depression and caregiver burden screening. The majority of patients (73%) were assessed within a month of referral; 87% of patients were managed in primary care, with 13% referred for specialist consultation. Follow‐up appointments were recommended for 83% of patients assessed (305/368); 33% (N = 123) completed follow‐up appointments. Key themes related to context were: MINT model fits an existing care gap; Multi‐disciplinary collaborative approach; Systematic approach to diagnosis and management; and, the need for easily accessible care close to home. Facilitating factors included: Senior leadership and physician champion support; Access to and support from various disciplines; and Access to resources (funding, space). Identified challenges varied by province and included: Lack of resources (staff, funding, space) and HCP new to dementia care required time to assimilate new knowledge to practice. The clinics were seen as impacting dementia care with: Provision of better dementia care than usually available in primary care; More comprehensive assessment and care plans; Interprofessional collaboration; Increased care partner support, and Quicker access to assessment and diagnosis. Conclusions The implementation of the MINT model in three provinces with different health systems demonstrates its scalability and its potential to improve dementia care in differing jurisdictions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".