A study in persons later after stroke of the relationships between social participation, environmental factors and depression
Bibliographic record
Abstract
OBJECTIVE: To explore the impacts of social participation and the environment on depression among people with stroke. DESIGN: Cross-sectional survey. SETTING: Structured interviews in the participants' homes. SUBJECTS: Community-dwelling persons with stroke in the rural areas of China ( N = 639). INTERVENTIONS: Not applicable. MAIN MEASURES: Depression (Hamilton Rating Scale for Depression-6), activity and social participation (Chinese version of the World Health Organization's Disability Assessment Schedule 2.0), environmental barriers (Craig Hospital Inventory of Environmental Factors), neurological function (Canadian Neurological Scale). RESULTS: A total of 42% of the variance in depression was explained by the environmental barriers, neurological function, activity, and social participation factors studied. Social participation, services/assistance, and attitudes/support were directly related to depression; their standardized regression coefficients were 0.530, 0.162, and 0.092, respectively ( p ⩽ 0.01). The physical environment, policies, and neurological function indirectly impacted depression. Depression influences social participation in turn, with a standardized regression coefficient of 0.29 ( p ⩽ 0.01). CONCLUSIONS: Depression and social participation are inversely related. The physical environment, services/assistance, attitudes/support, and policies all impact post-stroke depression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".