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Record W2137170393 · doi:10.1002/jhm.2444

Co‐creating patient‐oriented discharge instructions with patients, caregivers, and healthcare providers

2015· article· en· W2137170393 on OpenAlexaff
Shoshana Hahn‐Goldberg, Karen Okrainec, Tai Huynh, Najla Zahr, Howard Abrams

Bibliographic record

VenueJournal of Hospital Medicine · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineHealth careHealth literacyMultidisciplinary approachInclusion (mineral)NursingAction planHealth information technologyMedical emergencyPsychology

Abstract

fetched live from OpenAlex

For hospitalized patients, the transition from hospital to home is frequently accompanied by a significant amount of information to absorb. The objective of this work was to engage patients, caregivers, and healthcare providers in codeveloping patient-oriented discharge instructions, (ie, a brief transition plan with information that patients want). Overseen by a multidisciplinary advisory team, a participatory action approach using mixed methods was employed. Although formal inclusion and exclusion criteria were not used, deliberate efforts were made to engage groups with language barriers and limited health literacy. Symbols were designed and validated with the patient groups to represent each section of information to make the form more understandable for these patients. A prototype was codesigned using an iterative process. The form has been adapted for use in multiple health settings and is currently undergoing a multisite pilot to evaluate its effect on patient and provider experience.

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)0.032
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.076
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.372
Teacher spread0.349 · 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 designQualitative
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

Citations51
Published2015
Admission routes1
Has abstractyes

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