A Voice for Ontario Teachers in Disaster and Emergency Planning: A Case study of Teachers’ Experiences During the COVID-19 Pandemic
Bibliographic record
Abstract
The ongoing COVID-19 pandemic revealed a stark disconnect within the Ontario education system between the decision-makers at the system level (the Ministry of Education and school boards) and classroom teachers tasked with implementing these decisions. Interviews for this study provided primary and secondary school teachers in central and southern Ontario an opportunity to share their experience teaching during an extended public health disaster. The teachers focused the conversation on their frustration at the top-down decision-making from the school board and Ministry of Education and expressed that their needs as teachers were not adequately considered in the planning and response to the COVID-19 pandemic. They explained a range of impacts resulting from the lack of inclusion in the decision-making, including the erosion of their ability to carry out classroom planning, the deterioration of the quality of education delivered, and the harm to their well-being. The findings of this research recommend that one way to address this gap in the Ontario education system and mitigate the damaging effects, such as those experienced during the ongoing COVID-19 pandemic, is for meaningful consultation between classroom teachers and system-level decision-makers. The sentiments expressed by the participants in this study are a call for emergency practitioners within the Ontario education system to do better when it comes to including the voices and needs of teachers in preparedness and response efforts during extended public health emergencies.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".