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Record W4404107901 · doi:10.22374/jclrs.v8i1.63

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)

2024· article· en· W4404107901 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Contact lens Research and Science · 2024
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Health Research
Canadian institutionsnot available
Fundersnot available
KeywordsCUSUMLearning curveOptometryScleral lensOphthalmologyLens (geology)MedicinePsychologyComputer scienceMathematicsStatisticsOpticsContact lensPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.506
GPT teacher head0.678
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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