EVALUATING THE LEARNING CURVE OF A NOVICE OPTOMETRY STUDENT IN SCLERAL LENS FITTING: A PROSPECTIVE QUANTITATIVE STUDY USING DELIBERATE PRACTICE AND CUMULATIVE SUMMATION (LC-CUSUM)
Notice bibliographique
Résumé
Background and Objective: To evaluate the learning curve of a novice optometry student in scleral lens fitting through deliberate practice and to objectively quantify the learning process using the LearningCurve-Cumulative Summation (LC-CUSUM) test, ensuring accurate and unbiased results. Method: The complexity of scleral contact lens fittings was assessed by categorizing subjects into regular and irregular cornea groups. A student enrolled in the Master of Optometry program conducted the fittings using a dedicated scleral lens record form (rubrics) designed to quantify the lens management approach. Prior to performing fittings independently, the student received four weeks of training from a contact lens expert, who also served as her guide for the study. This training period and the subsequent fittings were structured based on the principles of deliberate practice, with the student performing repeated diagnostic trials. A maximum of three diagnostic trials were performed for each subject to achieve the optimal fit. After each trial, the student completed a self-efficacy scale questionnaire to assess her perceived diffi-culty and clinical judgement skills, recording “FIRST trial scores” following the initial trial and ‘LAST trial scores’ after achieving the optimal fit. The guide consistently provided verbal feedback after each case throughout the fitting process as part of the deliberate practice methodology to enhance the student’s understanding of the fitting procedure while keeping the scores confidential to ensure unbiased self-as-sessment. Following the complete supervision of the fitting procedure, the guide evaluated the student’s clinical skills using a specially designed observation scale questionnaire, referred to as the ‘GUIDE scores.’ A seven-point Likert scale was used to rate the judgement for both the self-efficacy scale and observation scale questionnaire. The student’s LAST trial scores were subsequently compared with the GUIDE scores. Results: A total of 80 scleral lens fittings were evaluated. The Intraclass Correlation Coefficient (ICC) demonstrated excellent agreement between student-reported self-efficacy scores and guide-reported observation scores. The difference in self-efficacy scores between the initial and final lens fittings was statistically significant (p < 0.05), as determined by the Wilcoxon signed-rank test. The Learning Curve-Cumulative Summation (LC-CUSUM) chart revealed that learning stabilized after 26 fittings, marking a consolidation phase where minimal further improvement was observed beyond this point, and additional practice primarily helped to maintain proficiency. The average number of trials required per eye was higher in patients with irregular corneas than those with regular corneas. Conclusion: This study evaluated the learning curve of a novice optometry student in scleral lens fitting through deliberate practice, utilizing the LC-CUSUM test to quantify progress and assess skill acquisition objectively. Proficiency was achieved after 26 fittings, with additional trials needed for irregular corneas, underscoring the influence of patient characteristics on learning. These findings emphasize the importance of structured training, personalized feedback, and self-assessment in developing clinical competence. The insights contribute to advancing education and research in contact lens science by providing practical guid-ance for designing effective programs focused on planning, teaching, and learning about scleral lens fittings.
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 | 0,031 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».