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Record W2480772571 · doi:10.1097/ncq.0000000000000215

Development of Indicators to Measure Health Literate Discharge Practices

2016· article· en· W2480772571 on OpenAlexaff
Jennifer Innis, Jan Barnsley, Whitney Berta, Imtiaz Daniel

Bibliographic record

VenueJournal of Nursing Care Quality · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of TorontoCanadian Institutes of Health Research
Fundersnot available
KeywordsHealth literacyDelphi methodMeasure (data warehouse)Hospital dischargeHealth careNursingPatient dischargeLiteracySet (abstract data type)Discharge planningMedicineBusinessMEDLINEPsychologyGerontologyComputer sciencePolitical sciencePedagogyIntensive care medicine

Abstract

fetched live from OpenAlex

Health literate discharge practices meet the health literacy needs of patients and families at the time of hospital discharge and are associated with improved patient outcomes and reduced readmission. A Delphi panel consisting of nurses, other health care providers, and researchers was used to develop a set of indicators of health literate discharge practices based on the practices of Project RED (Re-Engineered Discharge). These indicators can be used to measure and monitor the use of health literate discharge practices.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.226
GPT teacher head0.581
Teacher spread0.356 · 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 designOther design
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

Citations7
Published2016
Admission routes1
Has abstractyes

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