Adapting music education to pandemic regulations: Conceptualizing the school demands-resource theoretical innovation through autoethnography
Bibliographic record
Abstract
The COVID-19 pandemic profoundly impacted the wellbeing of students and teachers around the world. Job demands-resource theory has been an integral theoretical framework to understand how workers navigate strenuous conditions. Further, the study demands-resource model was conceptualized to understand how students’ school-based responsibilities affect their wellbeing and performance. There is a gap, however, in unifying these models to understand how teachers’ and students’ wellbeing are co-influenced by school-based demands and resources. To address this, I conceptualized the school demands-resource model to explore the intersections of this phenomena. Through autoethnography, I reflected upon my own professional practices as a public school music teacher facilitating learning experiences during the COVID-19 pandemic. Findings generated provide new understandings into the intersections of job demands-resources and study demands-resources and how public health regulations impacted music program function. Increased school demands included physical distancing, teaching and learning without singing or movement, and increased sanitation of classroom materials, but were alleviated via successful job/study crafting. School resources were also affected by pandemic-related health protocols, including changes in classroom relationships, school materials, and self-efficacy. Implications for future research include exploring how classroom relationships and job/study crafting may optimize engagement and wellbeing in school music programs.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".