Academic success in undergraduate nursing education: An integrative review
Bibliographic record
Abstract
AIMS: The aim of this review was to synthesize and appraise the available literature regarding academic success in undergraduate nursing education. DESIGN: We used Whittemore and Knafl's five-stage framework for integrative reviews. Toronto and Remington's step-by-step guide to integrative reviews provided practical guidance in the review process. DATA SOURCES: A search was employed to retrieve relevant scholarly, peer-reviewed, English-language articles published between 2003 and 2023 using the databases APA PsycINFO, CINAHL, ERIC, Education Research Complete, and MEDLINE. REVIEW METHODS: The initial search located 2599 articles. After duplicate screening at title and abstract and full text levels using predefined inclusion and exclusion criteria, 40 articles were selected for inclusion. All included articles were critically appraised using the Mixed Methods Appraisal Tool for empirical studies and the Joanna Briggs Institute critical appraisal tools for the reviews and the theoretical papers. RESULTS: A review and synthesis of the articles revealed preadmission factors can impact academic success in undergraduate nursing education. However, most authors used narrow measures of success such as on-time graduation and exam performance. In most articles performance in the clinical environment was not considered measured as part of academic success. Few empirical studies used a theoretical framework, and the overall methodological quality of the included articles was mixed. CONCLUSION: Our findings suggest a gap in the literature regarding more inclusive approaches to measuring academic success in undergraduate nursing education. The dearth of qualitative studies and limited attention to how academic success is measured in the clinical learning environment suggest future research should focus on exploring student nurses' perceptions of success. Furthermore, researchers examining this topic are encouraged to utilize suitable theoretical frameworks to guide empirical studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".