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Record W4386600338 · doi:10.1016/j.pecinn.2023.100214

Patient and family perceptions of a discharge bedside board

2023· article· en· W4386600338 on OpenAlexafffundabout
Diana E. McMillan, D.B. Brown, Kendra L. Rieger, G. H. Duncan, Joseph F. Plouffe, Christian Amadi, Syed Hasan Raza Jafri

Bibliographic record

VenuePEC Innovation · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsHealth Sciences CentreUniversity of Manitoba
FundersHealth Sciences Centre Foundation
KeywordsThematic analysisPerceptionAcute carePsychologyContent analysisQualitative propertyMedical educationDescriptive statisticsNursingMedicineQualitative researchHealth careComputer science

Abstract

fetched live from OpenAlex

Objective: To explore patient and family perspectives of a discharge bedside board for supporting engagement in patient care and discharge planning to inform tool revision. Methods: = 5) across seven adult inpatient units at a tertiary acute care hospital in mid-western Canada. Thematic (interviews), content (board, organization procedure document), and framework-guided integrated (all data) analyses were performed. Results: Four themes were generated from interview data: understanding the board, included essential information to guide care, balancing information on the board, and maintaining a sense of connection. Despite application inconsistencies, documented standard procedures aligned with recommended board (re)orientation, timely patient-friendly content, attention to privacy, and patient-provider engagement strategies. Conclusion: Findings indicate the tool supported consultation and some involvement level engagement in patient care and discharge. Board information was usually valued, however, perceived procedural gaps in tool education, privacy, and the quality of tool-related communication offer opportunities to strengthen patients' and families' tool experience. Innovation: Novel application of a continuum engagement framework in the exploration of multiple data sources generated significant insights to guide tool revision.

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)0.011
metaresearch head score (Gemma)0.038
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.081
GPT teacher head0.381
Teacher spread0.301 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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