Stroke Care Observerships : An interprofessional Collaborative Approach To Support Transition Experiences
Bibliographic record
Abstract
Background:Persons with stroke often experience fragmented care involving multiple health care providers (HCPs) from different settings. Stronger relationships between HCPs across the system can improve care coordination and the transition experience for persons with stroke and their families. The Toronto Stroke Networks adapted and implemented a learning program called Stroke Care Observerships (SCOs) to foster learning and meaningful collaboration between HCPs across the system. This current work aims to examine the value and outcomes of SCOs in supporting seamless stroke care. Methods: With tools to support mutual goal-setting and reflection, six SCOs were implemented in Toronto since 2012 and involved seven programs representing acute, rehab and community-based services. These SCOs involved multiple professions, including clinical and administrative participants. Using thematic analysis, qualitative themes related to the value of SCOs will be abstracted from post-SCO reflections, debrief discussions, and evaluations.Results:Preliminary results revealed that participants reported value in SCOs to foster in-depth cross-continuum learning and collaborations on new initiatives that support stroke best practices and alignment with system and local priorities. Some suggestions to improve transitions included the use of cross-continuum assessment tools and resources, standard educational materials for patients and their families/caregivers, and the need for continued collaborations. Conclusions:Based on initial themes, the SCO program enabled planning of meaningful observerships to foster mutual learning about other care environments and build relationships between providers within the system. Learnings from this work will support broader application of SCOs to other sites and inform stroke system planning.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".