Changes in Physician Costs among High-Cost Transcendental Meditation Practitioners Compared with High-Cost Nonpractitioners over 5 Years
Bibliographic record
Abstract
PURPOSE: To determine whether the Transcendental Meditation (TM) technique can affect the physician costs of consistently high-cost people. DESIGN: Quasi-experimental, longitudinal, cost-minimization evaluation. This 14-year, preintervention-postintervention study retrospectively assessed government payments to physicians for treating the TM and no-treatment (NT) groups. SETTING: Province of Quebec, Canada. PARTICIPANTS: The highest-spending 10% of 1418 Quebec health insurance enrollees who practiced the TM technique were compared with the highest 10% of 1418 subjects who were randomly selected from enrollees of the same age, sex, and region. TM participants had chosen to begin the technique prior to choosing to enter the study. MEASURES: Annual payments to private physicians in all treatment settings. The Quebec government health insurance agency provided the total physician payments for each of the 2836 subjects from 1981 to 1994. Other medical expense data for individuals were unavailable. Data were adjusted for medical cost inflation. ANALYSIS: For each subject, least-squares regression slopes were calculated to estimate preintervention and postintervention annual rates of change in payments. The groups' means, slopes, and medians were compared using both parametric and nonparametric tests. RESULTS: Before starting meditation, the yearly rate of increase in payments to physicians between groups was not significantly different. After commencing meditation, the TM group's mean payments declined $44.93 annually (p = .004), whereas the NT comparison group's payments exhibited nonsignificant changes. After 1 year, the TM group decreased 11%, and after 5 years their cumulative reduction was 28% (p = .001). CONCLUSIONS: The results suggest the intervention may be an effective method for reducing physician costs. 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".