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Record W2047324218 · doi:10.1310/tsr1703-183

Community Navigation for Stroke Survivors and Their Care Partners: Description and Evaluation

2010· article· en· W2047324218 on OpenAlexaff
Mary Egan, Sharon Anderson, Janet McTaggart

Bibliographic record

VenueTopics in Stroke Rehabilitation · 2010
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of AlbertaUniversity of Ottawa
Fundersnot available
KeywordsStroke (engine)CoachingPsychological interventionMedicinePsychologyPhysical therapyGerontologyNursing

Abstract

fetched live from OpenAlex

PURPOSE: To describe and evaluate a Community Stroke Navigation program. METHOD: A pretest-post test evaluation design was used. Community-dwelling stroke survivors were offered the services of a Community Stroke Navigator to assist them and their care partners with ongoing needs. Services included case coordination, emotional support, "just in time" education, coaching, advocacy, and accompaniment. The community reintegration and physical and emotional well-being of the stroke survivors and their care partners were measured just prior to and 4 months following service provision. The Community Stroke Navigator's notes and qualitative interviews were analyzed to categorize the interventions. RESULTS: Forty-one stroke survivors and 32 care partners received navigation services. The stroke survivors had experienced their stroke 1 month to 30 years previously (mean 4.7 years, SD 6.4 years). Thirty-five stroke survivors and 26 care partners took part in both the pretest and posttest. Posttest results demonstrated a small improvement in community reintegration among the stroke survivors but no significant change in community reintegration on the part of the caregivers and no alteration in physical and emotional health among either stroke survivors or care partners. CONCLUSION: Community Stroke Navigation has the potential to make an impact on community reintegration.

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)0.002
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 score0.267
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.038
GPT teacher head0.349
Teacher spread0.311 · 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

Citations43
Published2010
Admission routes1
Has abstractyes

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