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Record W2097683193 · doi:10.1310/tsr1602-147

Creating a Supportive Environment for Living with Stroke in Rural Areas: Two Low-Cost Community-Based Interventions

2009· article· en· W2097683193 on OpenAlexafffundabout
Joanne M. Newell, Renée Lyons, Ruth Martin‐Misener, Cindy Shearer

Bibliographic record

VenueTopics in Stroke Rehabilitation · 2009
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchCanadian Stroke Network
KeywordsPsychological interventionStroke (engine)MedicineNeeds assessmentIntervention (counseling)Resource (disambiguation)GerontologyNursingPsychologySociology

Abstract

fetched live from OpenAlex

With the growing burden of chronic illness affecting aging populations, rural health systems are faced with unique challenges to support and promote health in their communities. The Yarmouth Stroke Project was a 5-year initiative aimed at improving health care services for stroke survivors in rural Nova Scotia, Canada. A needs assessment indicated a lack of support to self-manage stroke during community re-integration. The needs reported by stroke survivors and their caregivers included informational and emotional support. A logic model approach was used to frame program planning leading to the design of two low-cost interventions. The first, a Community Resource Guide, was developed to address informational needs and enable stroke survivors to access community-specific resources. The second intervention, designed to address the emotional support needs of stroke survivors and their caregivers, involved collection and publication of local narratives. The stories described the experiences of community members affected by stroke, offering practical knowledge and messages of hope. The resource guide and stories represent two low-cost strategies for supporting and promoting the health of people living with stroke in rural settings.

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)0.001
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 score0.045
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.321
Teacher spread0.300 · 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

Citations14
Published2009
Admission routes3
Has abstractyes

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