Bibliographic record
Abstract
The purpose of this study was to investigate the perspectives of experienced music therapists regarding their career longevity in the field. This study further aimed to determine if themes relating to career longevity in music therapy would emerge when gathering perspectives of experienced music therapists. Modified grounded theory was used to examine semi-structured interviews with four music therapists with 20 or more years of experience. The participants consisted of four music therapists drawn from members of the Canadian Association of Music Therapists. The results of the study were divided into four categories: The surveyed music therapists’ educational background and motivations to become music therapists; music therapist participant’s reported sources of struggle throughout their music therapy careers; music therapist’s perceived important factors contributing to their own career longevity; and music therapist participants’ advice to emerging music therapists, with descriptive statements describing career longevity among music therapists as the final product. Results of the study suggest that self-care, adaptability, advocacy, and commitment to music therapy as a unique mode of intervention were considered significant to career longevity in music therapy practice. Interpretations as well as potential implications for practice, education and research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".