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Spousal Caregiving for Stroke Survivors

2007· article· en· W2023893540 on OpenAlexaff
Ursula Eileen Coombs

Bibliographic record

VenueJournal of Neuroscience Nursing · 2007
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsGovernment of Newfoundland and Labrador
Fundersnot available
KeywordsSpouseFeelingOptimismPsychologyStroke (engine)Family caregiversInclusion (mineral)Clinical psychologyGerontologyMedicinePsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

Caregiving for a spouse who has survived a stroke has multidimensional implications for both the partner and the spousal caregiver. A phenomenological study was conducted to examine the experiences of spousal caregivers for stroke survivors. Eight spouses who met the inclusion criteria participated in the study. van Manen's (1997) approach was used to examine the spousal caregivers' experiences. Data were collected through audiotapes from semistructured interviews. The interviews were transcribed to form textual descriptions of the caregivers' experiences. Six interrelated themes emerged through data analysis: experiencing a profound sense of loss, adjusting to a new relationship with a spouse, taking on new responsibilities, feeling the demands of caregiving, having to depend on the support of others, and maintaining hope and optimism. This study contributes to healthcare providers' understanding and knowledge of spousal caregivers for stroke survivors, and supports the need for continued research in this area.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.352
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), 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

Citations81
Published2007
Admission routes1
Has abstractyes

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