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Record W47523812

Stroke rehabilitation: availability of a family member as caregiver and discharge destination.

2014· article· en· W47523812 on OpenAlexaff
Shahzad Tanwir, Katherine L. Montgomery, Vinjamuri Chari, Shanker Nesathurai

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsHamilton Health SciencesSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineRehabilitationStroke (engine)SpouseActivities of daily livingCaregiver burdenLogistic regressionCohortHealth carePhysical therapyEmergency medicineGerontologyDiseaseDementiaInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: In today's health care environment where resources are scarce discharge planning is an important component of resource allocation. Knowledge of the factors that influence discharge disposition is fundamental to such planning. Further, return to home is an important outcome metric related to the effectiveness of a stroke rehabilitation program. AIM: To test the hypothesis that the patients who have a caregiver at home willing to participate in the care of the patient discharged from a stroke rehabilitation unit are more likely to be discharged home given other predictive factors being the same. DESIGN: Retrospective cohort study using binary logistic regression analysis with outcome as discharge home vs. discharge not home after in-patient stroke rehabilitation. SETTING: Hamilton Health Sciences multidisciplinary integrated stroke program unit. POPULATION: During this period, 276 patients were admitted to the integrated stroke unit, of which 268 patients were living in the community prior to hospitalization. The remaining eight patients were admitted from a care facility, such as a nursing home or assisted living facility. Since a sample size of eight is too small, these patients were excluded from the analysis. As such, the analysis is based on the 268 patients who were living at home prior to the onset of stroke. METHODS: The data points collected during the study period were age, gender, days from stroke onset to rehabilitation unit admission, pre-stroke living arrangement (lived alone vs. lived with spouse, partner, or another family member), FIMTM at admission, type of stroke, laterality of impairment, and discharge destination (i.e., private dwelling vs. nursing home, assisted living facility, or back to acute care). RESULTS: As established by a number of previous studies, the most significant predictors of home as discharge destination was admission FIMTM. However, the second most important predictive factor for home discharge was prestroke living arrangement (lived alone vs lived with spouse/partner/other family member) as hypothesized by the authors. CONCLUSION: Literature is rich with studies showing functional independence to be the most important predictor of home as discharge disposition but our analysis shows that pre-stroke living arrangement, i.e., lived alone vs lived not alone is also an important predictor for patients to be discharged home after stroke rehabilitation. CLINICAL REHABILITATION IMPACT: If current discharge planning relies on the availability of a caregiver at home after discharge from in-patient stroke rehabilitation then it may be worthwhile to include these caregivers in the inpatient rehabilitation process, to prepare them for their loved one's return home. Additionally, once the patient is discharged home more resources should be made available to support caregivers in the community. This may include more home healthcare personnel training and availability along with respite care.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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

Citations55
Published2014
Admission routes1
Has abstractyes

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