Canadian Music Teachers’ Burnout and Resilience Through the Second Wave of the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic globally impacted teachers’ wellbeing as they adjusted their practices to accommodate physical distancing, online learning, and hybrid models. Coinciding these changes, music teachers were impacted by local health regulations and school divisional policy revisions prohibiting singing and playing wind instruments indoors. Consequently, music teachers were required to abruptly change their practice, were displaced to alternative locations (e.g., gymnasiums), and/or were required to use travelling carts to teach. Research into the impacts that COVID-19 policy changes had on school music remains limited in Canadian contexts. To provide insight into this phenomenon, the research question was formulated: “What are music teachers’ perspectives on how the COVID-19 pandemic impacted their sense of wellbeing?” To facilitate the inquiry, a mixed-methods approach was utilized via an online attitudinal survey, a questionnaire, and focus group discussions. In total, 218 music teachers across Manitoba, Canada participated in the online survey and completed the questionnaire while 21 music teachers participated in focus group discussions. Findings demonstrated that music teachers experienced significantly strenuous working conditions throughout the pandemic, resulting in many teachers considering early retirement or resignation. Despite these challenges, music teachers demonstrated considerable resilience as they navigated the educational landscape in the province.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".