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Record W2318322016 · doi:10.3928/01484834-20120615-04

Patient Safety Education: An Exploration of Student-Driven Contextual Learning

2012· article· en· W2318322016 on OpenAlexaff
Jessica Spence, Carol Enns, Nadia Vecherya, Heather Dean

Bibliographic record

VenueJournal of Nursing Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChecklistPatient safetyTeamworkContext (archaeology)CurriculumMedical educationFocus groupInterprofessional educationMedicineObjective structured clinical examinationNursingPsychologySafety cultureHealth carePedagogy

Abstract

fetched live from OpenAlex

Medical and nursing students organized a contextual interprofessional learning experience involving observation of surgical safety practices according to the parameters of the World Health Organization (WHO) surgical safety checklist. Students were oriented to patient safety principles, operating room (OR) protocol, and the WHO surgical safety checklist. One hundred thirty students participated in interprofessional OR visitations. Selected students participated in focus groups, during which feedback regarding educational value and OR observations was obtained: Students thought that patient safety education was more meaningful in a clinical setting, and the degree of interprofessional collaboration appeared related to individual factors. Focus group data collected provides a foundation on which future research can build. Areas of inquiry may include development of teamwork within the context of interprofessional education, examination of the role of students in developing their own curricula, and randomized comparisons of clinical-based and classroom-based approaches to surgical safety education.

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)0.006
metaresearch head score (Gemma)0.014
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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0050.003
Open science0.0020.006
Research integrity0.0010.002
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 head0.192
GPT teacher head0.518
Teacher spread0.326 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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