Designer's Corner - Alternative Therapies and Placebos: Conceptual Clarification and Methodologic Implications
Bibliographic record
Abstract
The past decade has witnessed increased public awareness and use of alternative therapies for the management of diseases and symptoms (Schwartz, Chesney, Irvine, & Keefe, 1997). Spiro (1997) relates the increased use of alternative therapies to the need for personal connection, belonging, and comfort. A variety of biochemical, psychophysiological, and psychological therapies are available, including homeopathic preparations such as sulphur for dermatoses (Linde et al, 1997), acupuncture, reflexology, massage therapy, therapeutic touch, and self-help approaches. Margo (1999) reports that alternative therapies are used by 20% to 50% of persons in industrialized countries, with some $14 billion being spent on such therapies in the United States. A large number of publications have been devoted to alternative therapies (e.g., Journal of the American Medical Association, 280, 1998); medical and nursing schools are offering courses in alternative therapies; and funding has been made available to systematically investigate the effects of alternative therapies (e.g., National Institutes of Health) (Kwekkeboom, 1997). Despite the interest in alternative therapies and the accumulating empirical evidence supporting their effectiveness, some scholars view these therapies with scepticism. Many consider them as placebos (Linde et al., 1997; Shapiro & Shapiro, 1997). The arguments for or against considering alternative therapies as placebos are based on differences in professional paradigms and perspectives on what constitutes a placebo
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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 | 0.213 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".