Exploring sleep and vision impairment through a Patient and Public Involvement and Engagement (PPIE) lens: insights from multiple stakeholders
Bibliographic record
Abstract
Background Patient and Public Involvement and Engagement (PPIE) enhances the relevance and impact of health research. Our multidisciplinary study integrated PPIE to co-design research on sleep disorders in individuals with vision impairment (VI). While sleep disruption is well recognised in those with no light perception (NLP), sleep issues in individuals with less severe VI remain underexplored. Methods Individuals with VI, caregivers, charities, academics, and healthcare professionals were engaged to shape the study design. Contributors, recruited through VI support groups, identified sleep as a priority and helped refine the research questions, methodology, and study materials. An accessible online Insomnia Severity Index survey, pilot-tested by the group, assessed sleep quality and impact on daily functioning. Focus groups were held online and in person, with detailed notes analysed thematically and validated by contributors. Survey data were analysed descriptively. Results Thematic analysis identified four key themes: (1) sleep as a major concern, (2) the impact on families, (3) varied experiences with melatonin, and (4) interest in non-pharmacological interventions. Contributors emphasised the need for inclusive and adapted digital and device-based solutions. Conclusions This PPIE-led study highlights the need for tailored, non-pharmacological sleep interventions for people with VI, including those with less severe VI. Findings reinforce the importance of co-producing accessible digital solutions to ensure equitable care. Ongoing collaboration with stakeholders will be essential to developing and evaluating future sleep interventions aimed at improving quality of life in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".