A Pilot Study of Visual Function Parameters and Their Relationship to Road Traffic Accidents among Drivers in Trinidad and Tobago
Bibliographic record
Abstract
BACKGROUND: Vision attributes to about 95% of the sensory requirements for driving. When driving, impaired visual functions will undoubtedly result in a delayed response to obstructions. The regulations governing the issuing and renewal of a driver’s license in Trinidad make no provision of a visual requirement assessment. The study was prompted by an upsurge in road traffic accidents in Trinidad and Tobago, a twin-island nation. AIM: This pilot study investigates the relationship between defects in visual functions and traffic accidents among drivers on the island of Trinidad. METHODOLOGY: This descriptive cross-sectional study utilizes a non-probability purposive sampling approach to enroll licensed drivers who had been driving within the island’s districts for the past five years, the study conducted at a Multi-Health-Training Facility in Trinidad’s Central Area. After completing a semi-structured questionnaire, participants were given a full eye examination. RESULTS: 128 licensed drivers, both male and female, were enrolled for this study with ages ranging from 20 to 70 years with a mean age of 40.9 years ± 13.3 years, in which majority (83.6%, n = 107) of the drivers did not perform an eye exam before obtaining/renewing their driver’s license. About 32% (n = 41) of these drivers were involved in an RTA in the last five years. There was a statistically significant association between colour vision defect (x² = 15.94, p = 0.00), contrast sensitivity with a logCS score of less than 1.52 (x² = 25.05, p = 0.00, Cramer’s V= 0.442) and the occurrence of road traffic accident. CONCLUSION: According to this study, one-quarter of drivers have been involved in a road traffic accident within the last five years, and three-quarters have never had an eye examination before obtaining or renewing their driver’s license. The relevance of designing and developing a screening protocol that can be easily applied to driving safety licensing policies were established in this study. Based on these findings, prospective driver’s license applicants must undergo a complete eye examination, to drastically reduce RTAs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".