Treating individuals with amputations in therapeutic massage and bodywork practice: A qualitative study
Bibliographic record
Abstract
Abstract Introduction Best practices for massage therapy and bodywork (TMB) treatment of individuals with amputations are not well established. Although anecdotal observations are available, they have limited applicability for informing effective massage therapy and bodywork approaches for individuals with amputations. This study is part of a multifaceted research program seeking to establish a foundation for education and investigation of TMB for amputation related conditions/symptomology. The purpose of this study was to understand how TMB practitioners approach and treat individuals with amputations and their perceptions of outcomes. The TMB practitioner perspective is important in informing the development of a TMB practice framework for people with amputation. Methods The methodology of this study was informed by the phenomenological approach to qualitative inquiry. Semi-structured telephone interviews were conducted between June and September 2015, recorded and transcribed. Analysis consisted of descriptive coding and themes emerged through an iterative process. Codes and themes were discussed and verified with the research team. Participants were invited to review developed themes to indicate the extent to which results accurately encompassed their experiences as TMB practitioners. Results Twenty-five community practicing, professional TMB practitioners from 16 states consented to participate and all completed one interview. Analysis identified four themes which indicated TMB practitioners: value touch and consider it a core aspect of treatment for individuals with amputations; operate under a core belief that individuals with amputations greatly benefit from TMB; and consider relief that stems from TMB to be multidimensional, including physical, mental, and emotional aspects; and, certain components of treatment approach are unique to amputation clients. Conclusions Findings support that individuals with amputation benefit from TMB, at least from the perspective of TMB practitioners. Findings of this exploratory research identify important questions regarding approaches to treatment and potential TMB effectiveness hypotheses for amputation populations. Next steps will consider TMB approach and effects from the perspective of those with amputation(s).
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".