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Record W2054538885 · doi:10.1161/strokeaha.107.486035

Urinary Incontinence After Stroke

2007· article· en· W2054538885 on OpenAlexafffundabout
Chantale Dumoulin, Nicol Korner‐Bitensky, Cara Tannenbaum

Bibliographic record

VenueStroke · 2007
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationInstitut Universitaire de Gériatrie de Montréal
FundersRéseau Provincial de Recherche en Adaptation-RéadaptationCanadian Stroke NetworkCentre for Interdisciplinary Research in Rehabilitation
KeywordsMedicineStroke (engine)Psychological interventionPhysical therapyVignetteUrinary incontinenceIntervention (counseling)RehabilitationPhysical medicine and rehabilitationNursingSurgery

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Urinary incontinence (UI) is a common and distressing problem after stroke. Although there is evidence of new, effective UI poststroke rehabilitation intervention, it is unknown whether occupational therapists (OTs)' and physical therapists (PTs)' actual practices reflect best practices. We sought to determine the extent to which OTs and PTs identify, assess, and treat UI after stroke and to identify personal and organizational predictors of UI problem identification, best-practice assessment, and intervention. METHODS: Six hundred sixty-three OTs (93% participation rate) and 656 PTs (87% participation rate) working in stroke rehabilitation in Canada were randomly selected and interviewed with a telephone-administered questionnaire. Each responded to a series of open-ended questions related to a generated case (vignette) of a typical client with stroke who was experiencing UI. RESULTS: Only 39% of OTs and 41% of PTs identified UI after stroke as a problem. Fewer than 20% of OTs and 15% of PTs used best-practice assessments, and only 2% of OTs and 3% of PTs used best-practice interventions. Working in Ontario, having allocated learning time, and doing university teaching were among the variables explaining between 6% and 9% of the variability in UI identification and assessment. CONCLUSIONS: Canadian OTs and PTs do not routinely identify poststroke UI as a problem, and best-practice assessments and interventions are underused.

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 score0.033
Threshold uncertainty score0.545

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 head0.009
GPT teacher head0.267
Teacher spread0.258 · 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

Citations70
Published2007
Admission routes3
Has abstractyes

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