O-389 TECHNOSTRESS, PRIVACY, AND MAKING CONNECTIONS: WOMEN, WORK AND HEALTH CONDITIONS FOR ONLINE TEACHERS
Bibliographic record
Abstract
Abstract Background Although work and employment researchers have increasingly turned to the growing phenomenon of working and communicating digitally rather than face-to-face, we lack insight into the impact of this employment shift on specific professions. Our study examines the work organization and health impacts for elementary and high school teachers, who are mostly women and increasingly teaching online and from home. Methods This presentation describes an international scoping review of international English-language and Bengali literature published 2013 to 2023 on the health effects on teachers of online teaching, which is the first step of our larger Canada-Bangladesh study. Our systematic search of 4 databases includes empirical, peer-reviewed studies. Results Burnout was a key issue for teachers working online. Studies showed that work stress was associated with achieving an adequate social connection with students and with feelings of inadequate teaching due to the online medium. Further challenges for teachers working online include technostress (limited skills, connectivity hassle), privacy (home noise and background activity), digital surveillance (from both employers and parents), and ergonomics (workstation setup). Women teachers working from home also face competing expectations about housework and childcare. Discussion In the context of digital teaching, schoolteachers are a gendered profession facing new challenges related to home-work balance, privacy, and surveillance. These conditions require new approaches to training and occupational health and safety conditions. Conclusion Our recently funded study investigates the work and health of teachers in Canada and Bangladesh. Our scoping review results describe distinct health challenges of digital teaching related to social connection, technology, privacy and ergonomics.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".