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Record W2688713566 · doi:10.1017/s0144686x17000526

Marriage after the transition to stroke: a systematic review

2017· review· en· W2688713566 on OpenAlexaff
Sharon Anderson, Norah Keating

Bibliographic record

VenueAgeing and Society · 2017
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClosenessSpouseStroke (engine)PsychologyQuality of life (healthcare)Qualitative researchGerontologyMedicinePsychotherapistSociology

Abstract

fetched live from OpenAlex

ABSTRACT In health and chronic illness, satisfying marriages promote wellbeing and life satisfaction, yet stroke research has focused on either the stroke survivor as the patient or the spouse as a care-giver. Using Pope, Mays and Popay's framework for synthesising qualitative and quantitative methods, we conducted a systematic review and synthesis of 39 peer-reviewed studies to determine what happens to marital relationships after one partner has suffered a stroke. All the articles examined the impact of stroke. Three overarching themes characterise the evolution of marriage after stroke: chaos in the marriage, work to re-establish the marriage and evolution of the marriages. While both the stroke condition itself and the survivors’ need for care undermined the emotional qualities of the relationship for some couples, about two-thirds were able to retain or regain the relationship closeness. As in other chronic illnesses, the relationship closeness and a couple's ability to collaborate contributed to the survivor's recovery and to the satisfaction with life of the stroke survivor and the spouse. Our results underscore the need to consider the quality of, and the qualities of, the relationship between stroke survivors and their spouses. Future research could include a greater focus on qualitative or mixed-methods approaches to explore the interactions between stroke survivors and spouses that impact the wellbeing of both partners.

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)0.000
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.371
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.037
GPT teacher head0.350
Teacher spread0.313 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

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