The information needs of music therapists: challenges of assessment in the clinical setting
Bibliographic record
Abstract
Objective This paper explores various methods that librarians can use to evaluate and meet the information needs of clinical music therapists (MTs). Methods A survey of the literature of music therapy (MT) found many empirical studies of MT but none describing how MTs commonly express and satisfy their information needs. As a means of assessing their information needs, we first examine the basic features of MT practice and then compare MTs' information needs with the well-documented needs of nurses. Results We believe that MTs and nurses exhibit similar basic information needs, such as access to (i) colleagues and experts; (ii) current information in print, electronic, and alternate formats; (iii) reference and ILL assistance from librarians; and (iv) library training to search databases and catalogues. Conclusions Librarians should collaborate with MTs to identify their specific information needs. To begin, library services can be built by allocating relatively few resources. To evaluate what is required in evidence-based MT, librarians and MT associations should administer a national needs-assessment survey or questionnaire.
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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 | 0.097 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".