Non-pharmacological interventions on depression among eye disease patients: A systematic review
Bibliographic record
Abstract
1 Abstract Background The objective of the current systematic review is to synthesize and qualitatively summarize data with regards to the effectiveness of non-pharmacological therapies in treating depression among visually impaired older adults. Methods MEDLINE, EMBASE, Cochrane Library, and CINAHL were initially searched on March 24, 2023, whereas Web of Science and all conference databases were searched on May 30, 2023. All databases were searched once again on June 16, 2025. Studies were uploaded to Covidence and following duplicate removal, 3509 studies proceeded to title and abstract screening. Studies that investigated the impact of non-pharmacological interventions to adults (aged 18 years old) with eye disease on depression were included. Studies administering both pharmacological therapies alone and in combination with non-pharmacological therapies were excluded. Review articles, editorials, case reports, and case series were excluded. Study quality was assessed using The Tool to Assess Risk of Bias in Randomized Controlled Trials, Tool to Assess Risk of Bias in Cohort Studies, and the Risk of Bias in Non-Randomized Studies – of Interventions (ROBINS-I). Following risk of bias assessment, study data were extracted and narratively synthesized. Results A total of 30 full-text articles were included. Outcome data such as depression score at each measurement timepoint, change in depression score at each measurement timepoint, effect measures for depression, and regression data on depression score were extracted and qualitatively analyzed. Conclusion Problem solving treatment (PST) appears to offer short term relief for depression, but further studies should be conducted to investigate the necessity of booster treatments to maintain long-term effectiveness. Physical activity shows promise in improving depression outcomes among visually impaired older adults, however, additional studies with stronger evidence from randomized control trials controlling for the amount and type of exercise are necessary before reaching a firm conclusion.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".