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Record W4412516687 · doi:10.1177/10482911251360367

Unequal Occupational Conditions When Work Turns Digital: The Case of Schoolteachers

2025· article· en· W4412516687 on OpenAlexaffabout
Ellen MacEachen, Jennifer R. Whitson, Stéphanie Premji, Janice Aurini, Pamela Hopwood, Mary L. Miller, Yasmeen Almomani, Ishrat Sultana

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2025
Typearticle
Languageen
FieldPsychology
TopicTechnostress in Professional Settings
Canadian institutionsMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsWork (physics)Occupational safety and healthPsychologyOccupational exposureEnvironmental healthMedicineEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.042
GPT teacher head0.411
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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