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Record W4323795775 · doi:10.1177/23743735231160421

Empowering Patients With a Shared Communication Tool: A Patient-Oriented Multimethods Pilot Study

2023· article· en· W4323795775 on OpenAlexaffabout
Sharla King, Melanie Garrison, Manon Fraser, Michelle D. Wiley, Heidi Sharek, Sharon Gaine, Wanda Kosteroski

Bibliographic record

VenueJournal of Patient Experience · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsGlenrose Rehabilitation HospitalAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsPDCAJargonReport cardHealth careMedicinePsychologyNursingMedical emergencyQuality managementService (business)Business

Abstract

fetched live from OpenAlex

Not all patients feel empowered to take on the expanding role as active members in their healthcare journey. Healthcare services must shift attention to supporting patients and families in this emerging role. This support includes providing communication tools designed for patients and families to empower them to speak up. Two Plan-Do-Study-Act (PDSA) cycles were conducted to test a communication tool, the Jargon Alert!/WAIT card, with patients/families and providers in a Canadian rehabilitation hospital. After the first PDSA cycle, feedback from patients/families (n = 24), and providers (n = 4), informed modifications. The new Question Alert! card was retested in the same clinics. Patients/families (n = 13) reported the new card was a valuable tool enabling them to ask questions, although not all patients or family members expressed the need to use the card. The participating providers (n = 4) thought the Question Alert! card was helpful for quieter patients or family members who normally shy away from asking questions. The shared communication tool designed with patients improved the patient-centered experience and empowered patients/families to be more involved in their care.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.202
GPT teacher head0.474
Teacher spread0.272 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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