Psychosocial Adjustment to Visual Loss in Patients with Retinitis Pigmentosa
Bibliographic record
Abstract
PURPOSE: To investigate psychosocial adjustment to visual loss in patients with retinitis pigmentosa (RP). DESIGN: Cross-sectional study. METHODS: Thirty-three legally blind patients with RP participated in the study. Information regarding the patients' adjustment to their visual loss was obtained using the Psychological Adjustment to Illness Scale (PAIS-SR). Seven psychosocial domains were tested: health-care orientation, vocational environment, domestic environment, sexual relationships, extended family relationships, social environment, and psychological distress. These scores were compared with the psychosocial adjustment of patients with diabetes. RESULTS: Significantly elevated scores (a high score reflecting poor adjustment) were seen in four out of seven domains. The highest relative score was seen in the health-care orientation domain (65 +/- 14, p < 0.001), followed by vocational function (61 +/- 11, p < 0.001), social environment (58 +/- 9, p < 0.001), and extended family relationships (55 +/- 9, p < 0.05). The total PAIS score was significantly elevated (58 +/- 8; p < 0.001). CONCLUSIONS: Patients with RP have difficulties in adjusting to their visual loss particularly with respect to health-care orientation, vocational environment, social environment, and extended family relationships. They face more difficulties in these domains than diabetic patients in adjusting to their illness.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".