"How Do You Write Your Music Therapy Goals and Objectives?”: Seeking Canadian Perspectives
Bibliographic record
Abstract
The purpose of this exploratory research was to begin to understand how experienced Canadian music therapists write goals and objectives and to learn how the language we use reflects one’s therapeutic relationship with clients and the contexts in which we practise. The formation of goals and objectives as part of treatment planning is often considered to be an integral part of the work of music therapists, as seen in mainstream literature as a whole and—more specific to this study—in the practicum handbooks provided by Canadian music therapy training programs. To gain insight into how Canadian music therapists write their goals and objectives, a descriptive qualitative survey research design was used and responses from 19 experienced Canadian music therapists were analyzed using thematic analysis. A literature review of published music therapy writing and university teaching materials was completed. The study uncovered 19 ways that Canadian music therapists write—and do not write—goals and objectives, which correlates with the great diversity of music therapy practice in Canada. Six themes emerged when examining the respondents’ articulation of goals and objectives: the viewpoint of the therapist; the use of the word “will”; the direction of the therapeutic process; the use of qualitative, quantitative, and/or music-centred perspectives; the choice to not write goals and objectives; and the therapist’s use of domains. Study findings are discussed and ideas for further research are suggested.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.041 | 0.017 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".