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Record W1979193769 · doi:10.1310/tsr1506-593

Differences in the Experiences and Support Needs of Family Caregivers to Stroke Survivors: Does Age Matter?

2008· article· en· W1979193769 on OpenAlexaff
Scott D. Smith, Monique A. M. Gignac, Denyse Richardson, Jill I. Cameron

Bibliographic record

VenueTopics in Stroke Rehabilitation · 2008
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health NetworkQueen's University
Fundersnot available
KeywordsStroke (engine)GerontologyPsychologyPhysical medicine and rehabilitationMedicinePhysical therapyDevelopmental psychology

Abstract

fetched live from OpenAlex

PURPOSE: The rehabilitation stage of a stroke survivor's recovery provides an opportunity to prepare family caregivers for the supportive role they will play in the community. The goal of this qualitative study was to learn about family caregivers' experiences and support needs during the rehabilitation phase to inform program development. METHOD: We recruited family caregivers within the first 6 months post stroke. Nine caregivers participated in 40- to 60-minute in-depth qualitative interviews where the personal needs of caregivers were illuminated. Data were analyzed using content analysis. RESULTS: An overriding theme was differences in personal needs between older and younger caregivers. We interviewed five younger caregivers (55 years of age) and four older caregivers (>55 years of age). Younger caregivers identified informational support and training as important parts of their social support whereas older caregivers did not. Younger caregivers were also more likely to complain or criticize the health care system and staff than older caregivers. A common theme among older caregivers was to focus on the importance of keeping a positive outlook throughout the experience. CONCLUSION: Caregiver experiences and support needs varied according to age. This suggests that support programs should consider age as a factor when tailoring interventions.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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

Citations26
Published2008
Admission routes1
Has abstractyes

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