The Perfect Storm: Stress, Anxiety, and Burnout in the Secondary School Music Classroom
Bibliographic record
Abstract
This study investigates teacher stress, anxiety, and burnout through my experience teaching music in a suburban Toronto secondary school between 2002 and 2008. Primary data sources include a rich collection of journal entries I have written over a six-year period, which were retrospectively analyzed in this study. Hence, this study is principally rooted in reflective practice. In addition, this study is informed through autobiographical and phenomenological lenses. These other two lenses have allowed me to incorporate secondary source data (anecdotal notes, emails, text messages, and video footage) that were repurposed for this study. Findings have exposed two principal thematic representations from the data, namely; (a) those that directly or indirectly addressed extracurricular performances, and (b) fear of failure. Reexperiencing my experience has been exceedingly therapeutic and cathartic for me, providing insight and transparency into the demanding nature of music pedagogy at the secondary school level. In addition, findings have helped me to refine and develop my current and future pedagogy as a teacher educator.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".