Staff perceptions of using outcome measures in stroke rehabilitation
Bibliographic record
Abstract
PURPOSE: The use of standardised outcome measures is an integral part of stroke rehabilitation and is widely recommended as good practice. However, little is known about how measures are actually used or their impact. This study aimed to identify current clinical practice; how healthcare professionals working in stroke rehabilitation use outcome measures and their perceptions of the benefits and barriers to use. METHOD: Eighty-four Health Care Professionals and 12 service managers and commissioners working in stroke services across a large UK county were surveyed by postal questionnaire. RESULTS: Ninety-six percent of clinical respondents used at least one measure, however, less than half used measures regularly during a patient's stay. The mean number of tools used was 3.2 (SD = 1.9). Eighty-one different tools were identified; 16 of which were unpublished and unvalidated. Perceived barriers in using outcome measures in day-to-day clinical practice included lack of resources (time and training) and lack of knowledge of appropriate measures. Benefits identified were to demonstrate the effectiveness of rehabilitation interventions and monitor patients' progress. CONCLUSIONS: Although the use of outcome measures is prevalent in clinical practice, there is little consistency in the tools utilised. The term "outcome measures" is used, but staff rarely used the measures at appropriate time points to formally assess and evaluate outcome. The term "measurement tool" more accurately reflects the purposes to which they were put and potential benefits. Further research to overcome the barriers in using standardised measurement tools and evaluate the impact of implementation on clinical practice is needed. IMPLICATIONS FOR REHABILITATION: • Health professionals working in stroke rehabilitation should work together to agree when and how outcome measures can be most effectively used in their service. • Efforts should be made to ensure that standardised tools are used to measure outcome at set time-points during rehabilitation, in order to achieve the anticipated benefits. • Communication between service providers and commissioners could be improved to highlight the barriers in using standardised measures of outcome.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".