Disrupting Contact Lens Dropout: Practice-Centered Factors That Influence Continued Wear
Bibliographic record
Abstract
Contact lens dropout rates remain a significant challenge in eye care, with up to one in four new wearers discontinuing use within the first year. This study, commissioned by the Contact Lens Institute and conducted by Prodege, surveyed 401 U.S. adults to identify factors influencing contact lens retention. The survey categorized participants into new wearers (less than two years) and long-term wearers (two or more years) to assess differences in satisfaction, dissatisfaction, and the impact of eye care practice interactions. Results indicated a 19-point satisfaction gap between new (67%) and long-term wearers (86%), highlighting the need to improve early experiences. Key satisfaction drivers included comfort, visual quality, and convenience, while dissatisfaction stemmed from cost, handling, comfort, and vision issues. New wearers were more influenced by interactions with all practice staff, emphasizing the importance of comprehensive support and education. The study recommends proactive communication, personalized care, and lifestyle-centered prescribing to enhance retention. Addressing cost, handling, and comfort challenges through targeted actions can mitigate dropout risks. By fostering a supportive environment and leveraging the entire practice team, eye care professionals can improve patient satisfaction and promote long-term contact lens use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".