Exploring perspectives on the management of patients with complex care needs in stroke rehabilitation: An interpretive description study
Bibliographic record
Abstract
BACKGROUND: Exploring the "wicked" problem of improving care for patients with complex care needs could benefit a large swath of health system stakeholders given the breadth and depth of this issue. Patients with complex health and social needs often require customized care that deviates from expected care trajectories. At Canadian Stroke Distinction sites, clinicians provide care for a high proportion of patients with complex needs while adhering to best practice recommendations. METHODS: We conducted an interpretive description study, which explored the perspectives of 16 stroke rehabilitation clinicians, four organizational key informants, and two health system key informants. We collected data via 45- to 60-minute virtual interviews and engaged in a hybrid inductive-deductive approach to analysis. RESULTS: We constructed three main themes: (a) recognizing complexity is routine work for clinicians, (b) clinicians use workarounds to manage complexity, and (c) clinicians perceived and worked to bridge a difference between organizational processes and the realities of patient care. When comparing clinician and key informant perspectives, we noted differences regarding their perceptions of the prevalence and nature of patient complexity. We developed the concept of "work-as-expected" as an intermediary to bridge the gap between the "work-as-imagined" and "work-as-done" framework. CONCLUSION: We describe the strategies used by expert clinicians to continually manage care for a high proportion of patients with complex care needs. Although expert clinicians have developed effective workarounds, they experience significant moral distress when these strategies are unable to compensate for health system limitations. PRACTICE IMPLICATIONS: A better understanding of how clinicians manage the needs of patients with complex care needs could support policymakers and organizational leaders to consider macro- and meso-level strategies to support the adaptive practices of clinicians.
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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 | 0.044 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".