IN-CAM Outcomes Database - Its Relevance and Application in Massage Therapy Research and Practice
Bibliographic record
Abstract
One of the most commonly used complementary and alternative medicine (CAM) modalities in North America is massage therapy (MT). Research to date indicates many potential health benefits of MT, suggesting that ongoing research efforts to further elucidate and substantiate preliminary findings within the massage profession should be given high priority. Central to the development of a sound evidence base for MT are the use of valid, reliable, and relevant outcome measures in research, and practice in assessing the effectiveness of MT. The purpose of the present article is to introduce MT researchers and massage therapists interested in using outcome measures in research and clinical practice to the IN-CAM Outcomes Database website by describing the Outcomes Database and identifying its utility in MT research and practice. The IN-CAM Outcomes Database is a centralized location where information on outcome measures is collected and made accessible to users. Outcome measures are organized in the database within the Framework of Outcome Domains. The Framework includes health domains relevant to conventional medicine and CAM alike, and health domains that have been identified as important to CAM interventions. Users of the website may search for information on a specific outcome measure, plan research projects, and engage in discussions related to outcomes assessment in the CAM field with other users and with members of the CAM research community. As the MT profession continues to evolve and move toward evidence-informed practice, the IN-CAM Outcomes Database website can be a valuable resource for MT researchers and massage therapists.
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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 | 0.045 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".