Use of Practice-Based Research Networks in Massage Therapy Research
Bibliographic record
Abstract
Massage therapy is a profession, not simply an intervention, and pathways are needed to connect all key massage therapy profession components-clinicians, patient/clients, and the work-to the scholarship and research that describes, investigates, and shapes practice. While the volume of massage-related research has grown over the past few decades, much of the growing massage evidence base is not reflective of real-world massage therapy, nor is research typically conducted through the clinical lens of the massage therapy discipline. This situation reflects the unfortunate disconnect between massage therapy research and massage therapy practice, while magnifying a key research infrastructure deficiency within the massage therapy discipline: the who and where research is conducted is disconnected from the who and where massage therapy is practiced. Practice-based research networks (PBRNs) are a staple of primary care and other health professions research reflecting real life, discipline-focused practice that seeks to address the needs of the discipline's practitioners and patients. The PBRN model fits well with the directional need of massage therapy research. This paper presents a commentary on the use of PBRNs in massage therapy research, and the current state of PBRN research within the field of massage therapy, namely the recently launched MassageNet PBRN.
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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 | 0.488 | 0.562 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.023 | 0.148 |
| Scholarly communication | 0.040 | 0.068 |
| Open science | 0.008 | 0.035 |
| Research integrity | 0.027 | 0.040 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".