Are specific, measurable, action-oriented, realistic, and time-bound (SMART) goals associated with improved walking outcomes for stroke survivors undergoing outpatient stroke rehabilitation? An observational cross-sectional retrospective cohort study
Bibliographic record
Abstract
ABSTRACT Background Goal-setting is a core principle used in clinical practice to guide treatment. Setting goals improves adherence to rehabilitation treatments and may lead to better outcomes in people with neurological disorders. However, there is a lack of research into the prevalence of goals using a Specific, Measurable, Action-Oriented, Realistic, and Time-Bound (SMART) framework. Additionally, it is currently unclear if the SMART framework improves ambulatory outcomes in outpatient stroke rehabilitation. Methods This observational, cross-sectional, retrospective cohort study reviewed charts of all patients admitted to outpatient stroke rehabilitation at three hospitals over a 1-year period. Patients were included in the analysis if they had documented ambulatory goals. Goals were classified as either SMART or non-SMART. Analysis of covariance was used to compare Functional Ambulation Category scores at discharge between the SMART and non-SMART groups, controlling for admission scores, length of stay, and time post-stroke. Results 300 patients were included in the analysis. Of these, 61 (20.3%) had at least one SMART ambulatory goal. Analysis of covariance revealed a statistically significantly greater Functional Ambulation Category scores at discharge for the SMART goal group compared to the non-SMART group (mean Functional Ambulation Category scores at discharge [95% confidence interval], SMART group: 4.2 [4.0, 4.5], non-SMART group: 3.8 [3.6, 4.1]; F 1,60 = 4.40, p = 0.043). Conclusion The use of SMART goals in outpatient stroke rehabilitation is associated with better ambulatory outcomes compared to non-SMART goals. These findings suggest that incorporating the SMART framework in clinical practice can enhance the effectiveness of rehabilitation interventions for stroke patients. Further studies are recommended to explore the long-term effects and broader applications of SMART goal-setting.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".