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Record W2326146239 · doi:10.1177/0269215516641300

A study in persons later after stroke of the relationships between social participation, environmental factors and depression

2016· article· en· W2326146239 on OpenAlexaboutno aff
Lifang Zhang, Minghong Sui, Tiebin Yan, Liming You, Kun Li, Yan Gao

Bibliographic record

VenueClinical Rehabilitation · 2016
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)Stroke (engine)PsychologyPsychiatryClinical psychologyMedicineGerontologyEnvironmental healthEngineeringEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.377
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations47
Published2016
Admission routes1
Has abstractyes

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