Impact of Meditation on Improving Quality of Life Among Glaucoma Patients: A Study Protocol
Bibliographic record
Abstract
Purpose: The primary objective of this study is to assess the feasibility of administering an online meditation program compared to usual care to glaucoma patients to improve mental health outcomes such as quality of life, depression, anxiety, and sleep quality. The secondary objective of this study is to improve mental health outcomes among glaucoma patients. Patients and Methods: This is a study protocol for a 12-week randomized control trial employing a mixed methods design. Glaucoma patients will be recruited from the Ivey Eye Institute in St. Joseph's Hospital Center. Eligible patients will include glaucoma patients of at least 75 years of age. Consented participants will be randomized to an online meditation group or usual care group in blocks of 4, or 2 per arm. Patients assigned to the online meditation group will undergo guided meditation sessions led by a meditation instructor via Microsoft Teams. Patients randomized to the usual care arm will undergo their usual glaucoma treatment. All study questionnaires including feasibility metrics, SF-12, CES-D, HADS-A and PSQI will be administered to both treatment groups at week 1, week 3, week 6, and week 12. Following study conclusion, feasibility metrics will be sent to participants in the intervention arm. In addition, patients randomized to the usual care arm will be given the opportunity to enroll in the meditation program after the 12 week study period, but no data will be collected. Discussion and Conclusion: To the best of the authors' knowledge, this study is the first of its kind to assess the feasibility of administering an online meditation intervention to glaucoma patients. If the findings from this study demonstrate positive results in favor of the treatment, this will be used to justify larger clinical trials and eventually, the integration of online meditation into the standard of care for glaucoma patients.
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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 | 0.037 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".