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Creating an improvement culture for enhanced patient safety: service improvement learning in pre-registration education

2010· article· en· W1503727029 on OpenAlexfundno aff
Angela Christiansen, Linda Robson, CHRISTINE GRIFFITH-EVANS

Bibliographic record

VenueJournal of Nursing Management · 2010
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsnot available
FundersMcGill University
KeywordsService (business)NursingOrganizational cultureMedicineNursing managementPatient safetyMedical educationService-learningPsychologyBusinessPedagogyHealth carePublic relationsMarketingPolitical science

Abstract

fetched live from OpenAlex

AIM: The present study reports a descriptive survey of nursing students' experience of service improvement learning in the university and practice setting. BACKGROUND: Opportunities to develop service improvement capabilities were embedded into pre-registration programmes at a university in the Northwest of England to ensure future nurses have key skills for the workplace. METHODS: A cross-sectional survey designed to capture key aspects of students' experience was completed by nursing students (n = 148) who had undertaken a service improvement project in the practice setting. RESULTS: Work organizations in which a service improvement project was undertaken were receptive to students' efforts. Students reported increased confidence to undertake service improvement and service improvement capabilities were perceived to be important to future career development and employment prospects. CONCLUSION: Service improvement learning in pre-registration education appears to be acceptable, effective and valued by students. Further research to identify the impact upon future professional practice and patient outcomes would enhance understanding of this developing area. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse Managers can play an active role in creating a service culture in which innovation and improvement can flourish to enhance patient outcomes, experience and 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.313
Teacher spread0.304 · 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 designOther design
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

Citations26
Published2010
Admission routes1
Has abstractyes

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