An Ecological Approach to Activity After Stroke: It Takes a Community
Bibliographic record
Abstract
BACKGROUND: Biopsychosocial recovery from stroke is remarkable for some individuals, but the majority of stroke survivors have difficulty resuming activities. Even survivors with mild disability become disengaged. METHODS: Situational analysis grounded theory and ecological models were used to examine the barriers and facilitators to choice of everyday activities of stroke survivors aged 50 to 64 years. RESULTS: Resuming activities was an iterative process of scaffolding small tasks into activities through bargaining for access to practical support and inclusion into social situations. Although participants geared up to manage their condition and access activities, for the most part they were not in charge of the services and supports they required. They had little control over who was accepted to rehabilitation, for which services they qualified or disability policies. CONCLUSIONS: There are layers of interactions between individuals and multiple factors in their environments that influence participation. Low poststroke activity levels may be amenable to intervention. Further research should consider the following: (1) participation in activities through the lens of all levels of the socioecological model; (2) the impact of disability and aging-related stigma; (3) the results of ad hoc community navigation; and (4) the effects of restrictive health and disability policies on meaningful activity.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".