Measuring Optimal Psychological Conditions for Teaching and Learning in Post-COVID-19 Education
Bibliographic record
Abstract
The sudden outbreak of the novel coronavirus (COVID-19) and the declaration of a pandemic caused many rapid changes to educational systems around the world in March 2020. Many issues were encountered during the transition from on-campus to online teaching and learning approaches. With educators and policymakers focusing on how best to provide quality education and scrambling to ensure that appropriate technology and teacher training were in place, mental health issues became increasingly prevalent. Positive education approaches that build on existing strengths are essential to ensure both student and teacher well-being. The Positive Workplace Framework (PWF) is an example of how a strength-based approach can improve well-being in schools. By implementing practices related to one’s basic mental fitness needs and promoting team resiliency assets, schools can create optimal conditions that allow everyone to thrive and be at their best. The Mental Fitness and Resiliency Inventory (MFRI) is a validated questionnaire that provides a snapshot of a school’s well-being practices, as well as a profile from which to structure plans for enhancing collective well-being among staff and students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".