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Older people’s perception of their readiness for discharge and postdischarge use of community support and services

2012· article· en· W2096871528 on OpenAlexaboutno aff
Alice Coffey, Geraldine McCarthy

Bibliographic record

VenueInternational Journal of Older People Nursing · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
FundersHealth Research Board
KeywordsPerceptionHospital dischargeMedicineDischarge planningQuarter (Canadian coin)Older peopleGerontologyNursingPsychology

Abstract

fetched live from OpenAlex

AIM: To examine older patients perception of their readiness for discharge from hospital to home and use of community supports postdischarge, including readmission. BACKGROUND: Early discharge leaves little time for older people, families and professionals to prepare. The perspectives of patients are essential to therapeutic caring; however, few studies have examined patient's perception of their readiness for discharge. DESIGN: A quantitative, descriptive and correlational design was used. Data were collected from older patients (n = 335) at discharge and postdischarge using the Readiness for Discharge Scale (Weiss & Piacentine; Journal of Nursing Measurement, 14, 2006, 163) and a Demographic and Community Resource Questionnaire. FINDINGS: At 6 weeks postdischarge, almost one-quarter had been readmitted. Family support had increased, yet a minimal increase in formal services was found. At discharge, differences in readiness existed between the younger and older old. Significant relationships existed between lower perception of readiness at discharge and increased use of informal and formal support post-discharge. Lower perception of readiness had a significant relationship with readmission in the older old. CONCLUSIONS: Perceptions of readiness reflect the patient's reality and may be significant to discharge preparation and arrangements for support. IMPLICATIONS FOR PRACTICE: Older patients' perspectives should be included in discharge decisions and in individualised approaches by nurses to discharge preparation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.051
GPT teacher head0.396
Teacher spread0.344 · 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

Citations79
Published2012
Admission routes1
Has abstractyes

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