Patching the robot: Perspectives about amblyopia and the feasibility of social robots supporting patching therapy
Bibliographic record
Abstract
SIGNIFICANCE: Social robots have potential applications in eyecare, including the treatment of amblyopia. Desirable functions and features were explored with caregivers whose children were prescribed patching for amblyopia. Caregiver perspectives about the feasibility of social robots supporting amblyopia patching will inform the subsequent design of a social robot for a clinical trial with children undergoing patching therapy. PURPOSE: To explore a new strategy for addressing suboptimal amblyopia patching adherence by gathering caregiver perspectives on amblyopia and the feasibility of using a social robot to support their child's patching regime. METHODS: Caregivers of children who were prescribed patching for amblyopia completed an online survey and an online individual, semi-structured interview. Caregivers were asked about their amblyopia knowledge and experiences. They were also asked to share their views about using social robots to help them understand the condition and support their child with adherence to patching therapy. Anonymized interview transcripts were evaluated using thematic content analysis. Data saturation determined the sample size. RESULTS: Seven caregivers displayed knowledge deficits about amblyopia and a 50% average patching adherence. Prior experience with amblyopia mitigated the attitudes toward amblyopia and its management. All caregivers believed their child would benefit from interacting with a social robot during their eye examinations. They held mixed views about using the robot to enhance their knowledge about amblyopia. The perceived desirable social robot functions included regulating emotions, educating, motivating, and entertaining the child, demonstrating the child's vision, and monitoring vision progress. Desirable features regarding attributes, appearances, and actions were identified (e.g., friendly, expressive, gentle, and wearing an eye patch). CONCLUSIONS: The findings underscore a need for enhanced patching adherence and caregiver amblyopia literacy. They uniquely highlight how prior experience with amblyopia can shape caregiver attitudes towards managing amblyopia. Social robots may offer an innovative option to address these problems. These caregivers identified desirable functions (e.g., regulate, educate, motivate, entertain, and monitor) and features (e.g., wear an eye patch) of a social robot interacting with children undergoing patching therapy. These perspectives will help to inform the development of a social robot for a subsequent clinical trial with children undergoing patching for amblyopia.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".