Vision screening for older people: the barriers and the solutions
Bibliographic record
Abstract
The need for detection of vision impairment in older people has been well established To help achieve such a goal, vision screening should be integrated into the provision of aged care services, health and Community organisations. This study aimed to facilitate access to vision screening for people aged 65 and over in the City of Whitehorse (Melbourne) through aged care, health and community services. In total 147 people within these organisations were trained to use a vision screening kit. However, it was estimated that only 20% of participants used the kit, citing a number of barriers: the major ones being time restraints, conflict with other duties, and wanting an outside organisation to perform resting. Overall out of 510 people, 442 (87%) could be tested and 169 (38%) wre detected with vision impairment. Of these, 40 (24%) were under care and 129 (76%) were referred for further examination. As a result of this study we recommended that vision screening be integrated into a services provision of care to older people and patients as part of a holistic approach to heath.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".