Evaluation of the Tzu Chi Institute for Complementary and Alternative Medicine's Integrative Care Program
Bibliographic record
Abstract
OBJECTIVE: There are an increasing number of clinics providing integrative health care using new and innovative delivery models. The purpose of this study was to quantitatively and qualitatively evaluate the Integrative Care Program offered at the Tzu Chi Institute for Complementary and Alternative Medicine, Vancouver, British Columbia, Canada. DESIGN: At enrollment, data are collected on demographics, health history, current health concerns and diagnoses, quality of life/health status (SF-36) and patient satisfaction. The measures are repeated 6 months into the program. Descriptive analysis was used to summarize the data. Focus groups were also included as part of the study design. RESULTS: Patients seeking integrative care are a highly complex population living with numerous comorbid chronic conditions. Although their baseline scores on the SF-36 are lower than Canadian population norms across all subscales, significant improvement occurred from baseline to 6 months. Qualitative data support that patients were pleased with the clinical care they received and aligned with the philosophical underpinnings of the program. DISCUSSION: This is one of the first studies to evaluate integrative health care. Studies like this are needed to develop appropriate methods to assess models of integrative health care delivery.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".