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Variables impacting on patients’ perceptions of discharge from short-stay hospitalisation or same-day surgery

2000· article· en· W2149680695 on OpenAlexaff
William S. Rowe, Mark J. Yaffe⃰, Carolyn Pepler, Iryna M. Dulka

Bibliographic record

VenueHealth & Social Care in the Community · 2000
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicinePerceptionDischarge planningHospital dischargePatient dischargeNursingFamily medicineMEDLINEPsychologyIntensive care medicine

Abstract

fetched live from OpenAlex

The paper presents components of a study (n = 929) that was designed to examine, at one specific point in time, the hospital experience of the patient and the patient's corresponding recovery at home. Variables that captured the hospitalization and recovery experience relate to the degree of patient involvement in decisions about their treatment and discharge plans. Levels of health and recovery-related information reported by patients and their level of confidence in ability to resume regular activities once home were also measured. In general, individuals reported what many would consider having received less than optimal levels of information about their illness and recovery at home. Many patients also reported that they neither participated, nor were consulted on their needs or perceptions during their hospitalization. Expectations were that problems that patients might experience once home would have their origins in problems from within the community. However, the community resources were found to be less implicated and hospital resources more so. This suggests the importance of examining institutional issues even when one is focusing on the delivery of community services.

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)0.003
metaresearch head score (Gemma)0.016
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.418
Teacher spread0.325 · 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

Citations19
Published2000
Admission routes1
Has abstractyes

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