Unequal Occupational Conditions When Work Turns Digital: The Case of Schoolteachers
Bibliographic record
Abstract
Telework has significantly increased in the aftermath of COVID-19 lockdowns. However, it is unclear how working conditions for the same job differ when work is conducted in-person versus remotely and online, and whether occupational health risks can be considered as equal across formats. Our study examined schoolteachers to understand online versus in-person teaching work and health conditions. Focus groups and interviews about online teaching took place in 2023 to 2024 with 47 Canadian teachers and tutors who teach from kindergarten to Grade 12. We identified 5 key areas where synchronous online teaching conditions diverted from traditional classroom teaching and posed particular occupational health challenges for teachers. These are digital surveillance, technostress, loss of control over the teaching environment, difficult student engagement work, and moral discomfort. We recommend that teachers' job contracts and collective agreements should distinguish between in-person and online teaching and take into account the different related risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".