A critical appraisal of professional competency frameworks: What guidance is provided for stroke rehabilitation clinicians managing ‘complexity’?
Bibliographic record
Abstract
Background: Given current health system trends, clinicians increasingly care for patients with complex care needs. There is a recognized lack of evidence to support clinician decision-making in these situations, as complex or multimorbid patients have been historically excluded from the types of research that inform clinical practice guidelines. However, expert clinicians at sites of excellence (e.g., Stroke Distinction sites) provide measurably excellent care. We sought to review profession-specific competency frameworks to locate information that may be supporting the development of clinician expertise when managing the care of patients with complex care needs. Methods: We conducted a review of the professional competency frameworks for core members of the inpatient stroke rehabilitation team, to determine the degree of guidance and/or preparation for the management of patients with complex care needs. We developed and applied an assessment rubric to locate references to patient complexity, multimorbidity and complexity theory. Results: Across the professional competency frameworks, there are some references to complexity at patient- and team-levels; there are fewer references to system-level complexity. We noted a lack of clear guidance for clinicians regarding the management of patients with complex care needs. Conclusion: Further research is needed to explore how clinicians develop expertise in the management of patients with complex care needs, as we noted minimal guidance in the professional competency frameworks. However, we suggest that integrating complexity-related language into professional competency frameworks could better prime novice clinicians for new learning in the workplace and ease their transition into working in a complex context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".