Mindfulness-based cognitive therapy intervention for the treatment of late-life depression and anxiety symptoms in primary care: a randomized controlled trial
Bibliographic record
Abstract
I would like to thank the patients/participants for their time and involvement in the study.Despite the difficulties each one of them faced, their efforts and commitment helped advance science and facilitated the implementation of clinical services for others in need.Thank you to all the clinicians from the CIUSSS who collaborated and provided me with their expertise.To all those who financed this project, such as the Drummond Foundation, the Lady Davis Institute, and donated to the Jewish General Hospital's Division of Geriatric Psychiatrythank you!.I want to also to thank my supervisor Dr. Rej for his patience and guidance in the process of writing this thesis.His commitment and dedication in implementing alternative approaches using research in Montreal's health care system has ameliorated the treatment for many patients.I want to give special thanks to Dr. Gabriela-Torres, who once dreamed about implementing alternative well-based scientific approaches and led the initial phases of this research project.Her passion for science, commitment, knowledge, and support made not only this project a reality, but led me to learn from her and successfully accomplish my master's requirements.I want to thank the Geriatric Psychiatry Division, administrative, psychiatrist, researchers, and student volunteers.The administrative staff, Cindy Lui and Hilary, were incredibly patient, kind, and available to offer help regarding administrative issues.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".