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Record W4388445321 · doi:10.1016/j.nepr.2023.103833

Learnings from nursing bridging education programs: A scoping review

2023· review· en· W4388445321 on OpenAlexaff
Denise M. Connelly, Nicole A. Guitar, Andrea N. Atkinson, Sarah Janßen, Nancy Snobelen

Bibliographic record

VenueNurse Education in Practice · 2023
Typereview
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsRegistered Nurses' Association of OntarioWestern University
Fundersnot available
KeywordsCINAHLNursingWorkforceThematic analysisScopusBridging (networking)WorkloadNurse educationMEDLINEMedicinePsychologyMedical educationQualitative researchPsychological interventionPolitical scienceSociologyManagement

Abstract

fetched live from OpenAlex

AIM: The aim of this scoping review is to summarize and critically evaluate research focused on nursing bridging education programs internationally. Specifically, this review addresses bridging from a: (1) Personal Support Worker (or similar) to a Registered Practical Nurse (or similar); and (2) Registered Practical Nurse (or similar) to a Registered Nurse. BACKGROUND: Nursing bridging education programs support learners to move from one level of educational preparation or practice to another. These programs can therefore increase nursing workforce capacity. Global healthcare systems have faced nursing shortages for decades. Moreover, the presently insufficient nursing workforce is confronting an ever-increasing volume of needed healthcare that is rising with the global ageing demographic shift. DESIGN: The Joanna Briggs Institute methods for scoping reviews, combined with Arksey and O'Malley's (2005) guidelines, were used with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). METHODS: MEDLINE (Ovid), CINAHL, EMBASE and SCOPUS databases were searched. Articles published in English that included Personal Support Workers, Registered Practical Nurses, Registered Nurses and/or nurses in similar categories who were studied through the process of a nursing bridging education program were included in the review. The study search was limited to papers published after 2005 (i.e., the beginning of nurse workload "overload" according to the Canadian Nurses Association). Braun and Clarke's (2006) thematic analysis was used in a content analysis of the included studies. RESULTS: A total of 15 articles published between 2005 and 2022 were included. Four themes were generated: (1) participating in bridging education programs fuels both professional and personal development; (2) nursing bridging education programs enhance diversity in the nursing workforce; (3) student nurses do not anticipate the challenges associated with participating in a bridging program; and (4) mentor-mentee connection promotes academic learning and successful completion of nursing bridging education programs. CONCLUSIONS: Despite experiencing challenges, participation in/completion of nursing bridging education programs leads to successful role transitioning and self-reported fulfillment of personal and professional aspirations. This review revealed the need for bridging programs to accommodate the unique needs of student nurses. Incorporation of support services, mentorship and faculty familiarity with varying nursing educational backgrounds facilitates role transitions by reducing the perceived challenges of bridging and promoting connection to foster learning. Nursing bridging education programs allow greater numbers of nurses to be trained to build workforce capacity and enable care for the world's rapidly ageing population.

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)0.012
metaresearch head score (Gemma)0.033
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 score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0110.011
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.075
GPT teacher head0.492
Teacher spread0.417 · 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

Citations14
Published2023
Admission routes1
Has abstractno

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