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Record W3045352183 · doi:10.1177/0008417420941975

A Qualitative Study of Stroke Survivors’ Experience of Sensory Changes

2020· article· en· W3045352183 on OpenAlexvenueno aff
Dua’a Akram Alwawi, Evan Dean, Ashleigh Heldstab, Lisa Mische Lawson, Jill Peltzer, Winnie Dunn

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2020
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsSensory systemStroke (engine)PsychologyProprioceptionQualitative researchPsychological interventionActivities of daily livingPhysical medicine and rehabilitationRehabilitationSensory processingOccupational therapyMedicineCognitive psychologyPsychiatryNeuroscience

Abstract

fetched live from OpenAlex

BACKGROUND.: Previous literature examined tactile and proprioceptive changes after stroke; however, the lived experience of changes in all sensory systems is still a gap in the literature. PURPOSE.: To gain understanding of stroke survivors' experience of sensory changes and how sensory changes impact participation in daily life activities. METHOD.: This study utilized a qualitative description method. Researchers used semi-structured interviews with probing questions. Inductive content analysis approach was used to analyze the data. Researchers recruited 13 stroke survivors ≤75 years old who participated in a community-based stroke program. FINDINGS.: Emerging themes included daily life impact of sensory function changes, and experience and timing of sensory changes. Participants experienced changes in various sensory systems including touch and proprioception, visual, auditory, and taste. Survivors also reported sensitivity to environmental stimuli. Sensory changes affect survivors' participation in different aspects of daily life activities. Most participants experienced sensory changes right after their stroke. IMPLICATIONS.: Results from this study inform health care providers about stroke survivors' sensory needs to help them design interventions that match their needs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.329

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 head0.226
GPT teacher head0.437
Teacher spread0.211 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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