The Impact of the Transcendental Meditation Program on Government Payments to Physicians in Quebec: An Update
Bibliographic record
Abstract
PURPOSE: To determine whether practice of the Transcendental Meditation (TM) technique can affect medical expenses. DESIGN: The evaluation was a quasi experimental, longitudinal, cost-minimization study. SETTING: Province of Quebec, Canada. SUBJECTS: This study involved 1418 Quebec health insurance enrollees who practiced the TM technique compared with 1418 subjects who were randomly selected from enrollees of the same age, sex, and region. TM subjects had chosen to begin the technique prior to learning about and choosing to enter the study. MEASURES: This 14-year, pre- and postintervention study retrospectively assessed government payments to physicians for treating the TM and comparison groups. Other medical expense data for individuals were unavailable. Data were inflation-adjusted. For each subject, least squares regression slopes were calculated to estimate pre- and postintervention annual rates of change in payments. We compared the groups' means and 1%, 5%, and 10% trimmed means (robust estimators) of the slopes. RESULTS: Before starting meditation, the yearly rate of increase in payments between groups was not significantly different (p > .17). After commencing meditation, the TM group's mean payments declined 1% to 2% annually. The comparison group's payments increased up to 11.73% annually over 6 years. There was a 13.78% mean annual difference (p = .0017). CONCLUSIONS: The results suggest that the TM technique reduced payments to physicians between 5% and 13% annually relative to comparison subjects over 6 years. Randomized studies are recommended.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".