How Contextual Constraints Shape Midcareer High School Teachers' Stress Management and Use of Digital Support Tools: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Persistent psychosocial stress is endemic in the modern workplace, including among midcareer high school (secondary comprehensive) teachers in England. Understanding contextual influences on teachers' self-management of stress along with their use of digital health technologies could provide important insights into creating more usable and accessible stress support interventions. OBJECTIVE: The aim of this study was to investigate the constraints on stress management and prevention among teachers in the school environment and how this shapes the use of digitally enabled stress management tools. METHODS: Semistructured interviews were conducted with 14 teachers from southern England. The interviews were analyzed using thematic analysis. RESULTS: Teachers were unanimous in their recognition of workplace stress, describing physical (such as isolation and scheduling) and cultural (such as stigma and individualism) aspects in the workplace context, which influence their ability to manage stress. A total of 12 participants engaged with technology to self-manage their physical or psychological well-being, with more than half of the participants using consumer wearables, but Web-based or smartphone apps were rarely accessed in school. However, digital well-being interventions recommended by school leaders could potentially be trusted and adopted. CONCLUSIONS: The findings from this study bring together both the important cultural and physical contextual constraints on the ability of midcareer high school teachers to manage workplace stress. This study highlights correlates of stress and offers initial insight into how digital health interventions are currently being used to help with stress, both within and outside high schools. The findings add another step toward designing tailored digital stress support for teachers.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".