Harmonious Healthcare Teams: What Healthcare Professionals Can and Cannot Learn from Chamber Musicians
Bibliographic record
Abstract
Background: As healthcare becomes increasingly team based, we need new ways of educating trainees to be collaborative team members. One approach is to look to other professions that have developed highly effective ways of collaborating. Doctors have already turned to musicians for specific lessons; however, as of yet, there has been little empirical study of the ways that musicians interact in ensembles, or analysis of how this might provide insights for healthcare. Our hypothesis is that healthcare teams might learn from understanding collaborative practices of chamber musicians.Methods and Findings: We undertook an exploratory study of professional musicians playing in non-conducted ensembles. We used semi-structured interviews to explore factors the musicians considered important for effective group function. The interviews were transcribed and coded thematically. We identified three prominent themes that have relevance for healthcare teams.Conclusions: The highly individual nature of each musical group’s identity suggests that a focus on generic interprofessional education skills development may be insufficient. Furthermore, musicians’ understanding of the fundamental role of non-melodic parts provides the possibility of more nuanced leadership models. Finally, essential differences between musicians’ interactions in rehearsals and performances highlight the importance of varied forms of group interactions.
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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.008 |
| 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".