The Experiences of Living with a Visual Impairment in Peru: Personal, Medical, and Educational Perspectives
Bibliographic record
Abstract
BACKGROUND: Nearly 5 million people in Peru live with visual impairments, many of which are irreversible. within addition to eye care services, these individuals could benefit from government services and rehabilitation to improve their quality of life and promote equitable, inclusive social participation. Although numerous government policies address this, little is known about their perception and implementation. METHODS: Semi-structured individual online interviews were conducted with 29 people (7 low vision, 12 blind, 6 educators/rehabilitators, 4 medical doctors) in Peru between July and November 2024. Each participant was asked to respond to the same 16 open-ended questions. Their transcripts were coded into themes in 5 domains: assistive devices, vision rehabilitation services, government assistance programs, accessibility for people with visual impairments, and eye care services. The themes were compared among members of each group. RESULTS: Themes from educators/rehabilitators aligned well with those with blindness but much less with ophthalmologists and those with low vision. Participants mentioned that assistive devices are not traditionally provided by the government. There was little mention of vision rehabilitation services, particularly from low vision participants. Additionally, participants with visual impairments mentioned a lack of sensitivity from teachers, employers, and transport drivers. Interestingly, none of the participants with visual impairments benefitted from financial assistance. CONCLUSIONS: Many of the barriers are societal, referring to the lack of understanding from the public in relation to employment, education, transportation, or the use of assistive devices. People with visual impairments and educators should be included in any policy decisions to promote equality for Peruvians with vision loss.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".