Exploring the well-being of professional female musicians: a self-determination theory perspective
Bibliographic record
Abstract
Background: This study investigated the well-being of 16 professional female musicians through the lens of Self-Determination Theory, focusing on the satisfaction of their psychological needs for autonomy, competence, and relatedness, as well as the unique challenges they encounter in their careers. Methods: Semi-structured interviews were undertaken and analyzed using thematic analysis. Results and discussion: Three broad themes and 10 sub-themes emerged from the interviews. The findings demonstrate that the well-being of female musicians is closely tied to the satisfaction of their psychological needs for autonomy, competence, and relatedness. Conversely, when these needs are frustrated, their well-being is negatively impacted. Other themes that emerged from the interviews are intrinsic motivation and the gender specific challenges within the music industry. Conclusion: The study highlights the need for supportive environments to enhance the well-being of female musicians (and performers as a whole), addressing both their psychological needs and the specific gender-related challenges they face.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".