Specialist and family physician collaboration: Insights from primary care‐based memory clinics
Bibliographic record
Abstract
Given limited available geriatric specialists and complexity of dementia care, there is a need for greater collaboration between primary care and specialists to better meet the needs of persons with dementia. Meaningful family physician-specialist collaboration has the potential to improve health outcomes, timely access to care and more appropriate healthcare resource utilisation. Primary Care Collaborative Memory Clinics (PCCMCs), which include specialist support, provide a significant opportunity for studying the family physician-specialist interface. This study aimed to explore the nature of collaborative relationships between memory clinic family physicians and specialists caring for persons with memory concerns in PCCMCs across Ontario, Canada. Family physicians (N = 71) attending an education session and specialists (N = 21) completed a survey in the fall of 2017 that measured frequency and amount of collaboration, perceptions of their relationship and identified factors that enable and challenge collaboration. Descriptive statistics were generated for quantitative data and themes for responses to open-ended questions were explored using descriptive qualitative content analysis. Specialists and memory clinic family physicians valued their collaboration particularly as related to capacity building for dementia care and desired more time devoted to collaboration. Identified enablers and barriers to collaboration have implications for further integration of specialist support to potentially support improved patient care and further build capacity in primary care to manage dementia care. Opportunities exist for expanding and more intentionally supporting how family physicians and specialists interact with the creation of more formalised processes to support optimal collaboration, including a clear delineation of roles, responsibilities and expectations, more formally planned and structured relationship building and monitoring, identifying and addressing unique barriers to collaboration and use of a variety of methods of communication. Study findings have implications for how specialists and family physicians communicate and collaborate in other programmes for complex chronic conditions.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".