Problematising risk in stroke rehabilitation
Bibliographic record
Abstract
PURPOSE: Following stroke, re-engagement in personally valued activities requires some experience of risk. Risk, therefore, must be seen as having positive as well as negative aspects in rehabilitation. Our aim was to identify the dominant understanding of risk in stroke rehabilitation and the assumptions underpinning these understandings, determine how these understandings affect research and practise, and if necessary, propose alternate ways to conceptualise risk in research and practise. METHOD: Alvesson and Sandberg's method of problematisation was used. We began with a historical overview of stroke rehabilitation, and proceeded through five steps undertaken in an iterative fashion: literature search and selection; data extraction; syntheses across texts; identification of assumptions informing the literature and; generation of alternatives. RESULTS: Discussion of risk in stroke rehabilitation is largely implicit. However, two prominent conceptualisations of risk underpin both knowledge development and clinical practise: the risk to the individual stroke survivor of remaining dependent in activities of daily living and the risk that the health care system will be overwhelmed by the costs of providing stroke rehabilitation. CONCLUSIONS: Conceptualisation of risk in stroke rehabilitation, while implicit, drives both research and practise in ways that reinforce a focus on impairment and a generic, decontextualised approach to rehabilitation. Implications for rehabilitation Much of stroke rehabilitation practise and research seems to centre implicitly on two risks: risk to the patient of remaining dependent in ADL and risk to the health care system of bankruptcy due to the provision of stroke rehabilitation. The implicit focus on ADL dependence limits the ability of clinicians and researchers to address other goals supportive of a good life following stroke. The implicit focus on financial risk to the health care system may limit access to rehabilitation for people who have experienced either milder or more severe stroke. Viewing individuals affected by stroke as possessing a range of independence and diverse personally valued activities that exist within a network of relations offers wider possibilities for action in rehabilitation.
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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 | 0.086 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.007 | 0.069 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| 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".