From ‘Pool Noodle Tag’ to Building Snow Forts: Experiential Outdoor Education as Pedagogical job Crafting During the COVID-19 Pandemic
Bibliographic record
Abstract
Background The coronavirus disease 2019 pandemic significantly impacted education systems across the world. Physical education teachers were particularly affected by public health mandates, as many were displaced to teach in virtual and/or outdoor contexts while their gymnasiums were co-opted to become classroom learning spaces. Purpose Our research objective was to explore how physical education teachers engaged in job crafting via their pedagogical practices to accommodate public health mandates throughout the pandemic. Method Through semistructured interviews, ten ( n = 10) Canadian physical education teachers unpacked their professional experiences. Interview discussions were textually transcribed and analyzed via coding and reflexive thematic analysis approaches. Findings We conceptualized these teachers’ pivoting to experiential education approaches as a form of pedagogical job crafting, efforts to not only support their students’ learning and engagement, but also to sustain their own occupational flourishing and sense of meaningfulness. By adapting their pedagogical practices, these teachers were able to circumvent various occupational stressors, supported their students’ learning and engagement, and demonstrated attitudinal developments regarding their own occupational roles and identities within the school system. Implications These findings may provide insight for future researchers to explore and better understand how pedagogical job crafting may support and sustain teachers’ practices, particularly during strenuous circumstances.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".