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Record W1555330858 · doi:10.22230/jripe.2013v3n1a65

The Timely Open Communication for Patient Safety Project

2013· article· en· W1555330858 on OpenAlexafffundvenueabout
Margo Paterson, Jennifer Medves, Nancy Dalgarno, Anne O’Riordan, Robyn Grigg

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2013
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsQueen's University
FundersHealthForceOntario
KeywordsPatient safetyPsychological interventionMedicineHealth careNursingFocus groupPerceptionAdverse effectSafety cultureMedical educationPsychologyBusiness

Abstract

fetched live from OpenAlex

Background: Concern is growing over increased numbers of adverse events experienced by patients when admitted to acute care hospitals in Canada due to breakdowns in communication. The purpose of the Timely Open Communication for Patient Safety (TOC) project was to create a culture of patient safety through enhanced interprofessional communication by developing resources for caregivers and patients. Methods and Findings: The research was framed by a mixed methods design that included pre- and post-surveys and focus groups, online educational modules, face-to-face activities, and the development of patient orientation materials. Three clinical sites participated in the study. The findings indicate that supporting healthcare teams to identify strengths, challenges, and future directions of communicating, clarifying roles, functioning, and collaborating, coupled with educational interventions that raise awareness of patient safety,may enhance patient safety. The study was limited by the absence of data regarding the incidence of adverse events during the research period. Conclusion: The data showed improvement in team members' perceptions of interprofessional collaborative practice within the participating Collaborative Learning Units (CLUs). If the CLU model of care is adopted within the healthcare system, the safety of patients/clients may improve.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.225
GPT teacher head0.621
Teacher spread0.396 · 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 designNot applicable
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

Citations9
Published2013
Admission routes4
Has abstractyes

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