Delivering Clinical Skin Examination Education to Nurse Practitioners Using an Internet-Based, Microlearning Approach: Development and Feasibility of a Video Intervention
Notice bibliographique
Résumé
Background Skin cancer is the most common cancer; survival of the most serious skin cancers and malignant melanomas depends on early detection. Early detection relies on accessibility to clinical skin examination (CSE). Primary care nurse practitioners (PCNPs) are well-positioned to conduct CSEs; however, they require further education on CSE and have time constraints for continuing education. A digitally delivered intervention grounded in microlearning is a promising approach to deliver new information over a brief period. Objective Our objective was to develop and explore the feasibility of implementing a 1-week digital video intervention with content on CSE skills, defined as melanoma risk assessment, head-to-toe skin examination, and pigmented lesion assessment, for PCNPs. Specific aims were as follows: (1) Aim 1: to develop three microlearning-based melanoma videos with content on CSE that are suitable for digital delivery to PCNPs in various formats and (2) Aim 2: to assess the feasibility of the video intervention, including enrollment and retention rates, adherence, and acceptability and usability of the video intervention. Methods For Aim 1, the research team created storyboards for videos that addressed each CSE skill. An expert panel of three dermatologists reviewed the storyboards and videos for relevance, comprehension, and clarity using the content validity index (CVI). The panel evaluated the usability of the video intervention delivery by Research Electronic Data Capture (REDCap) and Vimeo using the System Usability Scale (SUS) and technical video production using Beaudin and Quick’s Quality Evaluation of Video (QEV). Aim 2 evaluated enrollment and retention rates of PCNPs, based on metrics from previous studies of CSE in the literature, and video intervention adherence. SUS and the Attitudes toward Web-based Continuing Learning Survey (AWCL) assessed usability and acceptability. Results CVI scores indicated relevance and clarity for each video: mean scores ranged from 3.79 to 4, where 4 indicated the video was highly relevant and very clear. The integration of REDCap and Vimeo was usable: the SUS score was 96, where 0 was the worst and 100 was the best. The digital delivery of the videos was rated as exceptional on all five technical items: the mean score was 5, where scores ranged from 1 (poor) to 5 (exceptional). Of the 32 PCNPs who were sent emails, 12 enrolled (38%) and, out of these 12, 10 (83%) completed the intervention and the surveys. Video intervention adherence was ≤50%. Participants rated the usability as better (mean 85.8, SD 10.6; better=70-90) and favorably ranked the acceptability of the AWCL’s constructs of perceived usefulness (mean 5.26, SD 0.08), perceived ease of use (mean 5.40, SD 0.41), behavior (mean 5.53, SD 0.12), and affection (mean 5.77, SD 0.04), where scores ranged from 1 (strongly disagree) to 7 (strongly agree). Conclusions The video intervention was feasible to deliver to PCNPs using a digital, microlearning approach. The findings provide support for using the videos as an intervention in a future pilot randomized trial targeting behavioral CSE outcomes among PCNPs and other primary care providers.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».