MétaCan
Menu
Back to cohort
Record W1964363452 · doi:10.12927/hcq.2008.19647

Nursing Education: A Catalyst for the Patient Safety Movement

2008· article· en· W1964363452 on OpenAlexaffabout
Kim Neudorf, Netha Dyck, Darlene J. Scott, Diana Davidson Dick

Bibliographic record

VenueHealthcare Quarterly · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsSaskatchewan Polytechnic
Fundersnot available
KeywordsPatient safetyCurriculumNursingHealth careMultidisciplinary approachSafety cultureMedicineInclusion (mineral)Core competencyCultural safetyMedical educationPsychologyPedagogyPolitical scienceBusinessManagement

Abstract

fetched live from OpenAlex

Creating a culture of safety in healthcare systems is a goal of leaders in the patient safety movement. Commitment of leadership to safety in the Saskatchewan Institute of Applied Science and Technology (SIAST) Nursing Division has resulted in the development of the Patient Safety Project Team (PSPT) and a steady shift in the culture of the organization toward a systems approach to patient safety. Graduates prepared with the competencies necessary to be diligent about their practice and skilled in determining the root causes of system error in healthcare will become leaders in shifting the healthcare culture to strengthen patient safety. The PSPT believes this cultural shift begins with the education system. It involves modifications to curricula content, facilitation of multidisciplinary processes, and inclusion of theory and practice that reflect critical inquiry into healthcare and nursing education systems to ensure patient safety. In this paper the practical approaches and initiatives of the PSPT are reviewed. The integration of Patient Safety Core Curriculum modules for competency development is described. The policy for reporting adverse events and near misses is outlined. In addition, the student-focused reporting tool, the results and the implications for teaching in the clinical setting are discussed. Processes used to engage faculty are also addressed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.074
GPT teacher head0.418
Teacher spread0.344 · 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

Citations15
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueHealthcare QuarterlySame topicPatient Safety and Medication ErrorsFrench-language works237,207