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Record W2066141338 · doi:10.1108/09526860410557598

Identification of seniors at risk: process evaluation of a screening and referral program for patients aged ≥75 in a community hospital emergency department

2004· article· en· W2066141338 on OpenAlexaff
Rebecca Warburton, Belinda Parke, W.S. Church, Jane McCusker

Bibliographic record

VenueInternational Journal of Health Care Quality Assurance · 2004
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsMcGill UniversityFraser HealthUniversity of Victoria
Fundersnot available
KeywordsReferralEmergency departmentQuality managementMedicineMultidisciplinary approachMedical emergencyPopulationPatient safetyIntervention (counseling)Identification (biology)NursingHealth careOperations managementEngineeringManagement system

Abstract

fetched live from OpenAlex

Reports on the authors' experience with a patient safety quality improvement program, intended to reduce the incidence and severity of adverse outcomes for emergency department (ED) patients aged > or = 75. The Identification of Seniors at Risk scale was used for screening, and those at high risk were referred for appropriate intervention. The plan-do-study-act improvement cycle was followed, conducting process evaluation to diagnose and correct implementation difficulties. Reports that: implementing an ED screening and referral program is deceptively difficult; process evaluation multidisciplinary working group meetings are an essential improvement tool; screening inclusion criteria had to be adapted to the subject population in order to make efficient use of staff time; the screening questions and process required ongoing assessment, revision, and local adaptation in order to be useful; and high-risk screening in the ED is critical to a hospital system's ability to anticipate clinical problems; the plan-do-study-act improvement cycle is a practical and useful tool for improving quality and systems in a real care setting.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.055
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 head0.050
GPT teacher head0.449
Teacher spread0.399 · 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

Citations76
Published2004
Admission routes1
Has abstractyes

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