How do working conditions change for in-person versus digital work? The case of schoolteachers
Bibliographic record
Abstract
Abstract Background Telework has significantly increased in Europe and internationally in the aftermath of COVID-19 lockdowns, with estimates of 22% of Europeans now doing some form of telework. However, it is unclear how working conditions for the same job differ when work is conducted in-person versus digitally, and if occupational health risks can be considered as equal across formats. Our study addressed the profession of school teaching to examine online working conditions versus those for in-person teaching. Methods To allow for close consideration of employment contexts, a qualitative study was conducted. Focus groups and interviews took place in 2023-24 with 45 teachers and tutors who teach from kindergarten to secondary grades across Canada. Results We found that online teaching added to emotional labour of teaching work and involved new occupational exposures. These included challenges with engaging students via cameras and microphones, digital surveillance of teachers by parents and students, unwanted exposure to students’ intimate home lives, and technostress. Conclusions The work of being an online teacher was very different than teaching in-person, yet there has been slow recognition of working condition differences for understanding of OHS exposures and for collective agreements. Key messages • In the context of a significant expansion of telework, assumptions cannot be made that the job conditions and exposures are the same job when performed in-person or at-home. • These differences will need to be recognized in job agreements and occupational risk assessments.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".