Learning from ‘Experts’ at Canadian Stroke Rehabilitation Distinction Sites: Implications for the Management of Patients with Complex Care Needs
Bibliographic record
Abstract
Background: Health care systems use standardized approaches to effectively reduce costs and deliver high-quality care. However, there is a growing subset of “complex” patients whose care trajectories deviate from the expected clinical pathways. These patients often require customized care to address their multiple concurrent health and social issues. In stroke rehabilitation settings, clinicians provide care for a high proportion of patients with complex care needs. At Canadian Stroke Distinction sites, clinicians provide measurably excellent care in accordance with the Stroke Rehabilitation Best Practice Recommendations (SRBPR), despite the limited applicability of these recommendations for complex care needs. Methods: The primary purpose of this thesis was to understand how expert stroke rehabilitation clinicians recognize and manage the care for patients with complex care needs. A secondary aim was to explore how clinicians developed the expertise to manage the care of these patients. To address these aims, we (1) conducted a critical appraisal of professional competency frameworks for stroke rehabilitation clinicians, to assess the degree of guidance for managing complexity in practice; (2) conducted an interpretive description study, which explored the perspectives of stroke rehabilitation clinicians, organizational and health system key informants; (3) conducted a critical appraisal of the SRBPR to locate recommendations relevant to the care of patients with complex care needs. Findings: There is minimal guidance in macro-level sources (e.g., competency frameworks, SRBPR) to support clinicians in caring for patients with complex needs. From interviews, we learned that (1) recognizing complexity is routine work for clinicians, (2) clinicians used workarounds to manage complexity, and (3) clinicians perceived and worked to bridge a difference between organizational processes and the realities of patient care. We noted misalignment between clinician perceptions of most patients as complex, and organizational efforts to plan for complexity, which estimate 20% of patients to be complex. Conclusion: There is a need to better define and account for the needs of patients deemed as complex, at macro- and meso-levels, in ways that support clinicians to better leverage their expertise in care delivery.
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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 | 0.100 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.048 | 0.035 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".