High School Teachers’ Experiences of Consumer Technologies for Stress Management During the COVID-19 Pandemic: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Stress in education is an adverse reaction that teachers have to excessive pressures or other types of demands placed on them. Consumer digital technologies are already being used by teachers for stress management, albeit not in a systematic way. Understanding teachers' experiences and the long-term use of technologies to support stress self-management in the educational context is essential for meaningful insight into the value, opportunity, and benefits of use. OBJECTIVE: The aim of this study was first to understand teachers' experiences of consumer technologies for stress management. They were chosen by teachers from a taxonomy tailored to their stress management. The second aim was to explore whether their experiences of use evolved over time as teachers transitioned from working at home during lockdown to working full time on school premises. METHODS: A longitudinal study intended for 6 weeks in the summer term (2020) was extended because of COVID-19 into the autumn term, lasting up to 27 weeks. Teachers chose to use a Withings smartwatch or the Wysa, Daylio, or Teacher Tapp apps. In total, 2 semistructured interviews and web-based surveys were conducted with 8 teachers in South London in the summer term, and 6 (75%) of them took part in a third interview in the autumn term. The interviews were analyzed by creating case studies and conducting cross-case analysis. RESULTS: The teachers described that the data captured or shared by the technology powerfully illustrated the physical and psychosocial toll of their work. This insight gave teachers permission to destress and self-care. The social-emotional confidence generated also led to empathy toward colleagues, and a virtuous cycle of knowledge, self-compassion, permission, and stress management action was demonstrated. Although the COVID-19 pandemic added a new source of stress, it also meant that teachers' stress management experiences could be contrasted between working from home and then back in school. More intentional self-care was demonstrated when back in school, sometimes without the need to refer to the data or technology. CONCLUSIONS: The findings of this study demonstrate that taking a situated approach to understand the real-world, existential significance and value of data generates contextually informed insights. Where a strategic personal choice of consumer technology is enabled for high school heads of year, the data generated are perceived as holistic, with personal and professional salience, and are motivational in the educational context. Technology adoption was aided by the pandemic conditions of home working, and this flexibility would otherwise need workplace facilitation. These findings add to the value proposition of technologies for individual stress management and workforce health outcomes pertinent to educators, policy makers, and designers.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".