Determining post-pandemic organizational health in the education sector: A review of a school-based workshop programming intervention
Bibliographic record
Abstract
The purpose of this study is to evaluate the effectiveness of a post-pandemic, school-based workshop programming intervention developed by a national mental health organization, to support education sector employees as they navigate post-pandemic challenges. Using a qualitative approach, data were gathered through post-workshop interviews conducted during the 2022-23 school year, and analysed according to five key indicators of organizational health: connectedness, organizational commitment, well-being, recovery and resilience. Findings indicate that while some participants continued to report role strain in each of these areas, highlighting the need for improved worklife balance, the workshop intervention positively influenced employee well-being through enhanced awareness of mental health resources and increased capacity for supportive dialogue with colleagues. This was notable specifically, when participants were aware of their emotional resilience and able to manage it effectively. The study highlights the vital role of sensemaking in helping education sector employees interpret complex or challenging situations. The research demonstrates that understanding these nuances can better inform future programming aimed at reducing further stress, minimizing additional burnout and preventing potential staff turnover. Accordingly, practical insights are suggested to guide the development of initiatives that enhance employee well-being, strengthen individual resilience and reinforce organizational commitment within the sector, factors that ultimately contribute to more sustainable and supportive work environments in education.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".