Negotiating Consent: Exploring Ethical Issues when Therapeutic Massage Bodywork Practitioners Are Trained in Multiple Therapies
Bibliographic record
Abstract
INTRODUCTION: Obtaining informed consent from competent patients is essential to the ethical delivery of health care, including therapeutic massage and bodywork (TMB). The informed consent process used by TMB practitioners has not been previously studied. Little information is available about the practice of informed consent in a treatment-focused environment that may involve multiple decision points, use of multiple TMB therapies, or both. METHODS: As part of a larger study on the process of providing TMB therapy, 19 practitioners were asked about obtaining informed consent during practice. Qualitative description was used to analyze discussions of the consent process generally, and about its application when practitioners use multiple TMB therapies. RESULTS: Two main consent approaches emerged, one based on a general consent early in the treatment process, and a second ongoing consent process undertaken throughout the course of treatment. Both processes are constrained by how engaged a patient wants to be, and the amount of information and time needed to develop a truly informed consent. CONCLUSIONS: An understanding-based consent process that accommodates an acknowledged information differential between the patient and practitioner, and that is guided by clearly delineated goals within a trust-based relationship, may be the most effective consent process under the conditions of real practice conditions.
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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 | 0.206 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.045 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".