MétaCan
Menu
Back to cohort
Record W2997109629 · doi:10.5430/jnep.v10n4p26

Nursing students’ experiences of patient safety incidents and reporting: A scoping review

2019· review· en· W2997109629 on OpenAlexaffvenue
Sherry Espin, Nancy A. Sears, Alyssa Indar, Lenora Duhn, Karen LeGrow, Binita Thapa

Bibliographic record

VenueJournal of Nursing Education and Practice · 2019
Typereview
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsHumber PolytechnicSt. Lawrence CollegeQueen's UniversityToronto Metropolitan University
Fundersnot available
KeywordsCINAHLPatient safetyMEDLINENursingData extractionMedicineFoundation (evidence)Medical educationPsychologyPsychological interventionHealth care

Abstract

fetched live from OpenAlex

Background: Nursing students gain exposure to the realities of patient safety incidents (PSIs) during clinical placements. How students learn about PSIs and reporting within clinical placements remains to be explored.Methods: This scoping review addressed: What is known about nursing students’ understanding and experiences of PSIs and incident reporting while practising in a clinical setting? CINAHL, MEDLINE, Scholars Portal, and ProQuest Nursing and Allied Health databases were searched. Study selection and data extraction were conducted by two independent reviewers. Data were collated, summarized and reported narratively.Results: Fifty-one articles were selected. Themes include: (1) types of PSIs reported; (2) how students engage in PSI reporting; (3) student factors related to PSIs; and (4) environmental factors relevant to student experiences of PSIs.Conclusions: This scoping review provides a necessary foundation from which to build future studies, to best support students and educators in addressing safety incidents within a just culture paradigm.

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)0.011
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.058
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0150.017
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.424
GPT teacher head0.655
Teacher spread0.232 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nursing Education and PracticeSame topicPatient Safety and Medication ErrorsFrench-language works237,207