Enkätundersökning om behandling av ögonbesvär : En studie om hur yngre personer med ögonbesvär åtgärdar symtom och deras medvetenhet kring behandling
Bibliographic record
Abstract
The aim of this study was to examine if, and in that case how, people with eye problems remedy these. The aim of the study was also to examine how much awareness that exists regarding different treatment options and to find out how the persons have attained their information regarding treatments. An anonymous online survey was published on a shared webpage accessible for the students attending selected university programmes at the Linnaeus university. The survey included the Ocular Surface Disease Index (OSDI), questionnaire. Among the 41 participants in the study, three out of four had eye problems and out of these, three quarters treated their eye problems. The most common treatments were taking breaks from screen work, using eyedrops and rubbing the eyes. Half of the participants had not searched for any information about treatment and a quarter of the participants got their information about treatment from an optometrist. In conclusion: there was a good awareness of treatment for eye problems, since the majority of the participants used some kind of treatment for their eye problems. The largest proportion of participants who had found information regarding treatment for their eye problems had received it through an optometrist.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".