Factors describing community ambulation after stroke: a mixed-methods study
Bibliographic record
Abstract
OBJECTIVE: To develop a model of community ambulation after stroke based on: Canadian data from community-dwelling individuals post-stroke; the experiences and opportunities for community ambulation expressed by individuals with stroke; and current literature. The model presents a visual depiction of the relationships between the different factors of community ambulation after stroke. DESIGN: A quantitative/qualitative explanatory sequential mixed-methods design was utilized. Secondary data analysis with structural equation modeling resulted in a community ambulation model. Two focus groups of individuals with stroke were conducted to verify and explain the model. SETTING: Community. SUBJECTS: Quantitative data from 227 participants: 142 (63%) male; 63.4 (12.0) years of age and 2.6 (2.5) years post stroke. Eleven individuals participated in the focus groups: 6 (55%) male; 61.4 (6.9) years of age and 5.8 (3.3) years since stroke. MAIN MEASURES: Model variables: items from the EuroQol, Preference Based Stroke Index, gait speed, Reintegration to Normal Living Index, the Community Health Activities Model Program for Seniors, and the Geriatric Depression Scale. RESULTS: The model had reasonable fit with three latent variables: ambulation, gait speed, and health perceptions (normed χ(2)=1.8, root mean square error of approximation = 0.060 (0.043; 0.075)). Depression was also a component of community ambulation. Participants verified the model and added endurance and the environment as additional components. Participants used self-awareness and knowledge of the environment to engage in cognitive strategies related to community ambulation. CONCLUSIONS: A model of community ambulation after stroke was developed and verified. Recognizing important components of community ambulation may assist physiotherapists in determining community ambulation goals, needs, and opportunities in partnership with clients.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".