Identification of common features within massage therapists’ professional identity
Bibliographic record
Abstract
OBJECTIVES: In Ontario, Canada, MTs are regulated and have a common scope of practice. However, diverse practice settings and approaches to care create a need for MTs to articulate their professional identity. This study sought to answer, "what common features are foundational to the professional identity of MTs in Ontario?" METHODS: This quantitative research study was a part of a larger exploratory sequential mixed methods study. An online questionnaire-based cross-sectional study was conducted based on previous qualitative findings. MTs in Ontario, who held an active certificate, were invited to participate. Data were analyzed using descriptive statistics. RESULTS: The analysis revealed 17 common features that were endorsed by most respondents. Participants also reported thinking of themselves as healthcare professionals, rather than service providers, a feeling that was held across practice settings. Interesting and unexpected differences were noted in statements regarding the perception of MTs, areas of profession-specific knowledge, and the establishment of the therapeutic relationship. CONCLUSIONS: This study furthers an understanding of MTs' identity. Specifically, MTs consider themselves to be HCPs who are confident in their knowledge and abilities, especially their communication skills. They believe in providing individualized care and empowering their patients to take charge of their own health. Despite areas of overwhelming agreement, disagreement in endorsement was seen in areas such as MTs perception of their external image, use of evidence in practice, and the establishment of professional boundaries. These areas provide an opportunity for future research to continue to develop a body of knowledge regarding MTs professionalism and identity.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".