Bibliographic record
Abstract
This study surveyed music therapists (682 respondents from 1,890 survey recipients) to examine their experiences in terms of reported perceptions of their situations, their practices, their approaches, and their personal, work, and family concerns. A first report from this survey (Curtis, 2013) looked at the experiences of present-day music therapists in Canada and the United States. This follow-up report examines the experiences of those survey respondents who self-identify as community music therapists (103 of the 682 survey respondents). Of those respondents, 13.6% were men and 86.4% were women. Canadians accounted for 18.4% and respondents from the US accounted for 81.6%. From among the entire 682 respondents, significantly more Canadians (55.4%) self-identified as community music therapists in comparison with their US counterparts (15.3%; p<.05). Quantitative and qualitative analyses provided information concerning Canadian and US Community Music Therapy respondents in terms of their: demographic information; education and work situations; personal, family, and work concerns; perceptions of discrimination; and theoretical orientations. Emerged themes from the qualitative analysis of respondents’ thoughts on Community Music Therapy included: firm identification; identification with a caveat; community building/belonging; drawing from Community Music Therapy principles; formal track record; reducing stigma; and working with groups. A need for future research (e.g., surveys, interviews, auto-ethnographies, etc.) into the profiles of community music therapists practicing in other parts of the world was highlighted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".