Case Reports: A Meaningful Way for Massage Practice to Inform Research and Education
Bibliographic record
Abstract
Practice-induced challenges to massage research and education include those related to disparate training standards, requirements, and expectations across the US, North America, and internationally. These challenges should not overshadow the need for practice to inform research and education, especially in light of the move towards effectiveness research. What remains constantly applicable to massage practitioners of all locations and from all backgrounds are treatment details regarding the techniques used, client/patient characteristics, condition/issue of complaint, provider and client/patient expectations, and outcomes. Case reports provide a venue for this information to be shared across all practitioners, educators, and researchers. While many massage practitioners are not trained in scientific writing, preparing and publishing a case report need not be daunting, especially with writing partners when writing burden can be shared. Writing in isolation can be challenging, even for trained researchers. Perceived practitioner contribution and credit are not reduced when authorship in a manuscript is shared; rather it may be enhanced with an experienced partner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.015 | 0.039 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 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".