Variation determinants within massage therapists’ professional identity
Bibliographic record
Abstract
OBJECTIVES: Individuals have multiple, competing identities that vary in importance to the self. Professional identity is a way in which individuals attribute meaning to their contribution to society and is influenced by complex factors. Globally, the roles and responsibilities of massage therapists (MTs) vary, making it challenging to articulate a cohesive professional identity. This article describes the investigation into the variables which influenced response regarding MTs' professional identity in Ontario, Canada. METHODS: An online questionnaire was distributed to active MTs with available email addresses in the public register of the College of Massage Therapists of Ontario. Chi-square tests of independence were used to compare dependent variables with independent variables. Significance was adjusted post hoc, using Bonferroni's correction, to reduce the chance of a type I error occurring. The threshold for significance was adjusted from p≤0.05 to p≤0.01 as multiple analyses were conducted with a high response rate. RESULTS: The results provided insight into the variables associated with differences in responses. Variation was seen based on gender, primary practice setting, length in practice, additional education, additional roles within the profession, additional designation as a healthcare provider, and membership in the RMTAO (Registered Massage Therapists' Association of Ontario). CONCLUSIONS: assumptions regarding the inclusion of these demographic items that can inform decisions regarding enrollment of the sample and data analysis.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".