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Record W4210946659 · doi:10.1177/08445621211019261

Formal Health and Social Services That Directly and Indirectly Benefit Stroke Caregivers: A Scoping Review of Access and Use

2022· review· en· W4210946659 on OpenAlexaffvenue
Anna Garnett, Jenny Ploeg, Maureen Markle‐Reid, Patricia H. Strachan

Bibliographic record

VenueCanadian Journal of Nursing Research · 2022
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcMaster UniversityWestern University
Fundersnot available
KeywordsRespite careService (business)Caregiver burdenNursingFamily caregiversPromotion (chess)BusinessPsychologyGerontologyMedicineDementiaMarketingPolitical science

Abstract

fetched live from OpenAlex

Stroke can be a life altering event that necessitates considerable amounts of formal and informal care. The impacts of stroke often persist over time requiring ongoing support for stroke survivors. Family members provide the majority of care and experience many life changes as a result of their caregiving role including social, financial, employment and health impacts. Formal supports such as counselling, respite, and health promotion initiatives that directly benefit caregivers or benefit them indirectly through supporting the stroke survivor, are well-placed to help caregivers manage their caregiving role. However, to date little is known about formal service use by stroke caregivers and the factors that influence their service use. This scoping review provides a critique and synthesis of what is known about stroke caregivers' access and use of formal services intended to support them. Findings suggest that while services are available, caregivers' ability to use them are impacted by both facilitators and barriers. Facilitators included: sex, age, and having a higher household income (depending on services used). Barriers included: high cost, poor service quality and deficient knowledge/communication regarding service availability. This review highlights a significant gap in our knowledge of caregivers' experience in accessing and using formal services.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.759
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.277
GPT teacher head0.489
Teacher spread0.212 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations12
Published2022
Admission routes2
Has abstractyes

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