An exploratory analysis of the self-reported goals of individuals with chronic upper-extremity paresis following stroke
Bibliographic record
Abstract
PURPOSE: To classify the self-identified goals of individuals post-stroke with chronic upper extremity (UE) paresis, and determine if age, UE functional capacity and pre-stroke hand dominance influence overall goal selection. METHOD: Sixty-five subjects participated. Using the Canadian Occupational Performance Measure (COPM) to establish treatment goals, the top five goals were categorized using the Occupational Therapy Practice Framework into five categories: activities of daily living (ADLs), instrumental activities of daily living (IADLs), leisure, work and general UE movement. A Chi-square analysis determined if age, UE functional capacity (measured by the Action Research Arm Test) and UE hand dominance influenced individual goal selection. RESULTS: The majority of goals were in the ADL (37%) and IADL (40%) categories. A small percentage (12%) was related to general UE movement. Individuals with moderate UE functional capacity identified more ADL goals than those with higher UE functional capacity. There was not a difference between age and UE dominance across all five goal areas. CONCLUSIONS: Individuals with chronic UE paresis had specific goals that were not influenced by age or hand dominance, but partially influenced by severity. General UE movement goals were identified less than goals related to specific activities. IMPLICATIONS FOR REHABILITATION: Considering the specificity of individual goals following stroke, it is recommended that clinicians regularly utilize a goal setting tool to help establish client goals. It is recommended that clinicians further inquire about general goals in order to link upper extremity deficits to functional activity limitations. Age, upper extremity functional capacity and hand dominance have little influence on the rehabilitation goals for individuals with chronic paresis after stroke.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".