Integrative Health Care: How Can We Determine Whether Patients Benefit?
Bibliographic record
Abstract
OBJECTIVE: Evaluation of integrative health care (IHC) models is becoming increasingly important. One of the areas that requires further attention is the development of an appropriate set of outcome measures. The purpose of this study was: (1) to identify how cancer patients phrase and frame the beneficial outcomes they experienced from IHC, and (2) to develop recommendations for an appropriate outcome measures package for evaluation of IHC. DESIGN: This study involved two different parts: (1) a secondary analysis of qualitative data consisting of transcripts from 42 personal interviews and three focus groups from previous studies related to IHC use by cancer patients; and (2) a content analysis of goal-setting data collected from patients attending an IHC clinic to categorize the type and range of their treatment goals. RESULTS: Six types of benefits were identified: physical well-being, change in physiological indicators, improved emotional well-being, personal transformation, feeling connected, global state of well-being, and cure. Types of goals identified by patients confirmed these benefits and include: to improve state of being, to be cancer free, to have more energy, more effective pain management, and improved quality of life. CONCLUSIONS: A patient's perspective is crucial in understanding the process and outcomes of intentional selfhealing. Assessing self-identified goals suggests the need for patient empowerment through participation in outcome evaluation. We present recommendations for an appropriate outcomes package that is relevant, practical, and based on patient experiences.
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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 | 0.035 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".