Equity, Diversity, Inclusion, and Belonging in Canadian Music Therapy
Bibliographic record
Abstract
Issues of equity, diversity, inclusion, and belonging (EDIB) are urgent priorities for an increasing number of minoritized and allied music therapists. With the aim of addressing gaps in the current music therapy literature and inspired by the British Association for Music Therapy’s 2020 Diversity Report, the research team developed a questionnaire that was distributed to all Certified Music Therapists in Canada. This questionnaire asked for demographic data in addition to exploring Canadian music therapists’ perspectives, priorities, and concerns regarding EDIB within the professional landscape. This paper explores participants’ answers to three qualitative questions, where results from data analysis are delineated by three overarching themes: Power and Representation, The Role of Music, and Advocacy. We share our perspectives on key findings from the data analysis and connect our discussion to broader discourse surrounding systemic inequalities in healthcare, music practices, and society from our perspectives as minoritized and allied Canadian music therapists. We present ideas for future research and explore how our findings contribute to vital dialogue that challenges inequality, removes barriers, and supports progress toward becoming an inclusive profession that fosters belonging and represents the communities we serve.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.063 | 0.031 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".