Patients’ Experiences of Ending Massage Therapy Care: A Commentary
Bibliographic record
Abstract
Patients are best positioned to provide information about their experiences of healthcare services; however, their perspectives are often underutilized. During informal discussions with massage therapists (MTs), and through the authors’ own professional experiences, it was noted that there are times when patients decide independently, and without notice, to end the care they are receiving. To date, no research has been published exploring the experiences of patients who choose to discontinue massage therapy care and there is a gap in the quality assurance process of MTs. Lack of understanding of patients’ experiences is a missed opportunity to strengthen the therapeutic relationship, ensure patient safety, improve treatment quality, and develop professionally. To affect change within multiple levels of the profession so that patients’ experiences are valued and solicited in a manner that allows patients to provide critical feedback, we recommend two initial actions. First, we challenge massage therapy stakeholders to consider alternate research methodologies and methods to explore this phenomenon. Second, we recommend that MTs cultivate an appreciation for constructive criticism and actively listen to their patients with confidence.
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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 | 0.023 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.035 | 0.040 |
| 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".