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Record W2301199859 · doi:10.1007/s40037-016-0258-4

Developing an appreciation of patient safety: analysis of interprofessional student experiences with health mentors

2016· article· en· W2301199859 on OpenAlexaff
Sylvia Langlois

Bibliographic record

VenuePerspectives on Medical Education · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsThematic analysisPatient safetyFocus groupMedical educationHealth careInterprofessional educationNarrativeQualitative researchMedicinePsychologyQuality (philosophy)NursingSociology

Abstract

fetched live from OpenAlex

INTRODUCTION: A critical task for health profession educators is to foster student appreciation of patient quality and safety issues. Although instructional methods vary, few focus on the direct communication of the patient experience to students. This qualitative study explores the experiences and learning of health profession students participating in a Safety Module in the Health Mentor Programme. METHODS: Small interprofessional groups of students were paired with a health mentor, an individual experiencing chronic health challenges. Students followed a 90-minute, semi-structured interview format exploring issues regarding quality care and safety. Following the interviews, students participated in a facilitated asynchronous online discussion and completed a reflective practice paper. An inductive thematic analysis of both of these text-based datasets revealed emerging themes. RESULTS: Themes identified in the data included: Patient partnerships as critical to optimal care; consideration of a variety of safety issues; importance of advocacy in promoting safety; improvement of future practice enabled through patient perspectives on clinical error; and embracing of interprofessional communication and collaboration. CONCLUSIONS: The findings suggest that engagement with the health mentor narratives facilitated students' appreciation of quality and safety issues related to patient 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.476
Teacher spread0.441 · 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 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

Citations11
Published2016
Admission routes1
Has abstractyes

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