Creating & Maintaining Healthy Workplaces: Changes in Organizational Climate Post COVID-19
Bibliographic record
Abstract
Due to the COVID-19 pandemic and ensuing lockdown for most of the world beginning in March 2020, Work from Home (WFH) became the new normal. As we emerge from the pandemic, WFH and hybrid work continue at different levels in many organizations. Researchers have been noticing trends in the workplace that may be a residual impact from the work from home (WFH) model, including higher turnover rate, burnout, emotional mismanagement, busyness, workplace fatigue, and multitasking. As a result, it is important to examine the different organizational structures, processes, procedures, team functions, and individual roles that developed with telework and hybrid to see how they influenced leaders who now say they need more coaching, training, and mentoring to rebuild a psychologically safe workspace. We used a mixed methods approach to interview leaders of over 50 organizations in the USA and France to find which elements became part of the organizational climate, their hidden costs or benefits, and how they are shaping the future organizational culture. This PDW will review our initial findings, and work with the participants to learn what happened in other organizations, and how to help all our organizational clients move forward to create and maintain workplaces that are both healthy and productive as we put the worker front and center.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".