MétaCan
Menu
Back to cohort
Record W4386530542 · doi:10.11124/jbies-23-00192

Experiences of baccalaureate nursing students in preceptorship during the COVID-19 pandemic: a systematic review protocol

2023· review· en· W4386530542 on OpenAlexaff
Denise Thomas, Michelle Su, Madelayne Walter, Bernadette Zakher

Bibliographic record

VenueJBI Evidence Synthesis · 2023
Typereview
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of VictoriaLangara College
Fundersnot available
KeywordsCINAHLWorkforceCritical appraisalPandemicNursingMEDLINEMedicineLicensureMedical educationPracticumGrey literatureNurse educationCoronavirus disease 2019 (COVID-19)PsychologyAlternative medicinePolitical sciencePsychological intervention

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this review is to appraise and synthesize current evidence of the clinical experiences of baccalaureate nursing students in preceptorship during the COVID-19 pandemic. INTRODUCTION: Nursing education programs support quality clinical practice learning experiences, which are essential for preparing students for both the current and future workforce. The COVID-19 pandemic has drastically changed the health care system and, previous estimates of the global shortage of nurses have now almost doubled. Understanding nursing students' clinical experiences during the pandemic can assist with identifying the needs of the future workforce. Nursing students complete the final practicum, also known as the last clinical, internship, or preceptorship, before they are eligible to apply for licensure. This review seeks to explore these pre-transitional, unprecedented preceptorship experiences during COVID-19 to better understand how to prepare pre-licensure nurses for the altered workforce. INCLUSION CRITERIA: This review will include qualitative studies that address the clinical experiences of undergraduate nursing students in preceptorship during the COVID-19 pandemic from 2020 until the present. METHODS: The databases to be searched will include CINAHL, MEDLINE, ERIC, Google Scholar, and Embase. Reference lists of included studies will be reviewed to identify additional studies. Gray literature will be searched for via ProQuest Dissertations and Theses, Google, and GreyNet International. Unpublished studies will be searched for on websites, including those of national associations of nursing. Study selection, critical appraisal, data extraction, and data synthesis will be performed independently by 2 reviewers. The findings will be collated using meta-aggregation to produce comprehensive synthesized findings and a ConQual Summary of Findings. REVIEW REGISTRATION: PROSPERO CRD42022328303.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
grokno category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
opusno category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.066
metaresearch head score (Gemma)0.062
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.066
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.062
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0150.013
Bibliometrics0.0190.013
Science and technology studies0.0040.004
Scholarly communication0.0070.009
Open science0.0050.005
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0380.005

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 head0.099
GPT teacher head0.469
Teacher spread0.370 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJBI Evidence SynthesisSame topicNursing education and managementFrench-language works237,207