Learning from Canadian Stroke Rehabilitation Care Clinicians: Implications for the Management of Patient Complexity
Bibliographic record
Abstract
Introduction: Clinicians working in stroke rehabilitation settings care for a high proportion of patients with complex care needs. The average patient recovering from a stroke has a minimum of two co-morbidities and a range of psychological and socio-economic needs (Gallacher et al., 2019; Schaink et al., 2014). The care trajectories of these “complex” patients usually deviate from standard practices and they require customized care. There is limited evidence to guide clinician decision-making in relation to complex patients, as their needs are not fully reflected in clinical practice guidelines (Boyd et al., 2005). In lieu of evidence, clinicians may engage in collaborative problem solving to generate innovative solutions (Nelson et al., 2016). However, implementing customized approaches may be difficult in environments that prioritize adherence to specific clinical pathways. For example, Stroke Distinction sites across Canada are recognized for delivering care in accordance with the Canadian Stroke Best Practice Recommendations (CSBPR) (Accreditation Canada, 2021). We sought to explore how expert stroke rehabilitation clinicians provide customized care to a large subset of complex patients, while meeting organizational performance requirements at Stroke Distinction sites. Methods: We used an interpretive descriptive research design (Thorne, 2016) to explore the research question: How do expert clinicians at Stroke Distinction sites recognize and manage the care of patients with complex care needs? We interviewed 16 clinicians (including medicine, allied health, nursing), four organizational leaders and two health system experts. We collected data via 45–60-minute virtual interviews and engaged in a hybrid inductive- deductive approach to analysis. Results: We reported three themes: (1) recognizing complexity is routine work for clinicians, (2) clinicians use 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 differences regarding perceptions of patient complexity across participant types. For example, clinicians reported most of their patients to have complex care needs. They described care for patients with a high degree social complexity (e.g., limited family or financial support) as particularly difficult to manage. When unable to secure outcomes that patients “deserve”, clinicians reported experiencing moral distress. In contrast, the organizational and system experts described that stroke programs are designed for approximately 20% of patients to have complex care needs; this represents a large mismatch in perceptions of patient complexity in comparison with the clinician group. Conclusions and Implications: Expert clinicians use adaptive strategies to continually manage care for a high proportion of patients with complex care needs. However, they often report moral distress when these strategies are unable to compensate for health system limitations. Given the significant mismatch in perceptions of patient complexity between clinicians and leaders who shape systems of care, decision-makers could consider macro- and meso-level strategies to support the adaptive practices of clinicians in alignment with workforce strategies to prioritize the clinician retention. Next Steps: This research was a part of a doctoral dissertation. We continue to share this work and seek collaboration with others in supporting clinician management of complexity.
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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 | 0.084 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.054 | 0.028 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".