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Record W1967260214 · doi:10.3109/13561820.2014.891574

Patient safety and professional discourses: implications for interprofessionalism

2014· article· en· W1967260214 on OpenAlexaffabout
Paula Rowland, Simon Kitto

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity Health NetworkUniversity of TorontoThe Wilson Centre
Fundersnot available
KeywordsPatient safetyNursingWork (physics)Health careMedicineHealth professionsProfessional boundariesMedical educationPsychologyPolitical science

Abstract

fetched live from OpenAlex

Patient safety has been presented as a unifying concern across the health professions. This conceptual connection has been accompanied with efforts towards standardized, interprofessional safety competencies, as well as increased attention towards interprofessional education for systems improvement. Despite numerous program initiatives and research endeavors, progress towards improving patient safety in hospitals is viewed as disappointingly slow. This paper adds to a body of literature that suggests patient safety remains a difficult problem to solve because safety is not simply a technical issue, but is a practice embedded in organizational and professional contexts. In this paper, we explore the differences between the professions, as different professional groups intersect with the ways patient safety is thought about, talked about, and known about in an acute care hospital in Canada. We draw on findings from a critical discourse analysis of documents related to patient safety, as well as transcripts from interviews from (a) formal health care leaders and (b) practicing clinicians from medicine, nursing, occupational therapy, physiotherapy, and social work. This analysis suggests implications for the way different professions may or may not work with one another in the service of patient safety.

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)0.004
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.005
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 head0.032
GPT teacher head0.491
Teacher spread0.459 · 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 designTheoretical or conceptual
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

Citations48
Published2014
Admission routes2
Has abstractyes

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