Multi-Jurisdictional Evaluation of Sentinel City Virtual Simulation for Community Health Nursing Clinical Education
Notice bibliographique
Résumé
Although positive learning outcomes have been documented for nursing students who participate in virtual simulation for community health nursing clinical education (Chircop & Cobbett, 2020), it is unknown whether learning outcomes of students using the same virtual simulation program are comparable across jurisdictions. Nine schools of nursing across Canada (Nova Scotia, Ontario, British Columbia) implemented and evaluated Sentinel City a virtual simulation program to complement the traditional community clinical, or as an alternative learning experience. A descriptive survey was used to carry out an evaluation of the use of Sentinel City and student learning outcomes. Quantitative data provided demographic statistics to describe the sample, compare student learning outcomes and perceptions of their learning experience and the qualitative data from open-ended questions provided detailed responses on the use of Sentinel City and its future recommendation. Data were analyzed using ANOVA (Welch statistic) to identify any significant differences among students from each jurisdiction in relation to their perception of the use of Sentinel City in meeting their course learning outcomes. Qualitative data from open-ended responses were analyzed using the six-step process outlined by Braun and Clarke (2006). The use of Sentinel City for community clinical learning in various Canadian jurisdictions positively contributed to achieving desired student learning outcomes. There are, however, significant differences among jurisdictions. Most of the students “agreed” that Sentinel City helped them achieve course learning outcomes. In all jurisdictions, most of the students indicated that they were “confident” and “very confident” in their knowledge about the community health nursing process, understanding of a population/community health assessment, understanding how to plan a population health intervention, and in their ability to integrate the five principles of primary health care into practice. Regarding their ability to apply a population health perspective (upstream thinking), most of the students were “confident” and “very confident”. Almost all students (93.62%) were confident and “very confident” in their ability to recognize health inequities indicating the highest level of confidence (Mean 4.38, SD 0.71). As educators, we found several advantages with the use of SC, including the ability to create controlled and standardized clinical learning experiences which contributes to fairness and quality of community clinical education. We recommend a robust orientation and professional development program for clinical instructors in community health nursing that is consistent with the new International Nursing Association for Clinical Simulation and Learning (2021) standards. The required expertise in community health nursing together with solid foundational knowledge of a simulation program for community health nursing and facilitation skills competence during pre- and de-briefing sessions are necessary for student success. One of our recommendations has been achieved with the recent release of Sentinel City Canada (https://www.sentinelu.com/events/sentinel-city-canada/). Overall, this cross-jurisdictional study revealed a flexibility with which Sentinel City® can be used or adapted as a teaching tool at different programs across Canadian jurisdictions and still contribute to the achievement of course learning outcomes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».