O-197 COVID-19 AND HEALTH AND SAFETY OF THE DIGITAL EDUCATORS: A CASE STUDY OF BANGLADESH
Bibliographic record
Abstract
Abstract Introduction The COVID-19 pandemic led to significant changes in the education sector worldwide, forcing schools to shift to remote online teaching. As such, the digital teaching introduced unique occupational health stressors that posed a threat to the wellbeing of educators in Bangladesh. While much attention has been given to the quality of education, there is a lack of understanding regarding the impact on teachers’ lives and wellbeing. The study aimed to identify specific health risks and needs related to the transition to digital teaching. Methods Six Focus Group Discussions (n=48) were conducted to solicit the (primary and high school) teachers’ and tutors’ experiences of digital teaching. Using purposive sampling strategies, we recruited participants from different groups, including genders, rural and urban settings, and many more. Results The findings reveal the dynamics of challenges the teachers encountered, which included the blurring work-life, competing responsibilities (e.g.,caregiving), dual burden (e.g., household chores) for females while teaching remotely, access and learning new technology (e.g., technostress), anxiety stemming from salary suspension or job loss. However, many expressed a positive outlook because of the convenience of commuting to schools amidst traffic jams. Discussion In Bangladesh, neither Labor laws nor recently developed (2013) occupational health and safety (OHS) policies do not cover schoolteachers’ OHS. As the Ministry of Education has no specific guidelines, it is high time to identify occupational health risks exposed to digital educators. Conclusion Digital educators face numerous health-related challenges that need to be communicated with the existing policies to understand the gaps between policy and practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".