Current Evidence in Virtual and In-Person Objective Structured Clinical Examinations in Nurse Practitioner Education: A Narrative Review
Bibliographic record
Abstract
Abstract Aim: To review the efficacy of virtual and in-person Objective Structured Clinical Examinations (OSCEs) in nurse practitioner (NP) education. Scope: OSCEs are often used to evaluate the clinical competencies of NP students. Meanwhile, the COVID-19 pandemic caused a shift towards virtual OSCEs, prompting this review of literature. The utility, benefits, and drawbacks of virtual versus in-person OSCEs will be explored. Methods: This meta-narrative review examines existing literature focused on both virtual and in-person OSCEs, as well as publications addressing both methods. Nine peer-reviewed articles published between 2014 and 2024 were gathered from three electronic databases. Articles were limited to those focusing on operationalization of OSCEs and student evaluation and learning. Findings: Five overarching themes emerged. These included: the value of OSCEs, benefits and challenges of virtual and in-person OSCEs, student performance and learning, student satisfaction, and feasibility. Conclusion: Both virtual and in-person OSCEs have valuable roles in NP education. An approach that incorporates both methods is recommended to optimize resources, student learning, and assessment outcomes. A mixed approach leverages the logistical advantages and cost-effectiveness of virtual assessments with the hands-on experience, standardization, and motor skills training associated with in-person OSCEs. Keywords: Virtual OSCEs, in-person OSCEs, Nurse Practitioner Learning, Clinical Competency, narrative review. Donnees actuelles sur les examens cliniques objectifs structures en mode virtuel et en presentiel dans la formation des infirmiers praticiens : Une revue narrative Resume :. Objectif : Évaluer l'efficacite des examens cliniques objectifs structures (Objective Structured Clinical Examinations - OSCE) en mode virtuel et en presentiel dans la formation des infirmiers praticiens (IP). Portee : Les OSCE sont couramment utilises pour evaluer les competences cliniques des etudiants en formation d'infirmier praticien. Cependant, la pandemie de COVID-19 a entraîne un passage vers des OSCE virtuels, ce qui a amene a realiser cette revue de la litterature. L'utilite, les avantages et les inconvenients des OSCE virtuels par rapport aux OSCE en presentiel y sont analyses. Methodes : Cette revue meta-narrative examine les publications existantes traitant des OSCE virtuels et en presentiel, ainsi que des etudes comparant les deux approches. Neuf articles evalues par des pairs, publies entre 2014 et 2024, ont ete selectionnes a partir de trois bases de donnees electroniques. Seuls les articles traitant de l'operationnalisation des OSCE, de l'evaluation des etudiants et de l'apprentissage ont ete retenus. Resultats : Cinq themes principaux ont emerge : la valeur des OSCE, les avantages et defis des OSCE en mode virtuel et en presentiel, la performance et l'apprentissage des etudiants, la satisfaction des etudiants et la faisabilite des deux approches. Conclusion : Les OSCE virtuels et en presentiel jouent tous deux un rôle important dans la formation des IP. Une approche combinee est recommandee pour optimiser les ressources, l'apprentissage des etudiants et les resultats des evaluations. Cette approche mixte permet de tirer parti des avantages logistiques et de la rentabilite des evaluations virtuelles, tout en preservant l'experience pratique, la standardisation et l'entraînement aux competences motrices offerts par les OSCE en presentiel. Mots-cles : OSCE virtuels, OSCE en presentiel, apprentissage des infirmiers praticiens, competence clinique, revue narrative.
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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 | 0.013 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".