Building psychosocial safety climate in turbulent times: The case of COVID-19.
Bibliographic record
Abstract
Our theoretically driven cluster-randomized cohort control study sought to understand how psychosocial safety climate (PSC)-a climate to protect worker psychological health-could be built in different organizational change scenarios. We drew on event system theory to characterize change (planned vs. shock) as an event (observable, bounded in time and space, nonroutine) to understand how events connect and impact organizational behavior and features (e.g., job design, PSC). Event 1 was an 8-month planned intervention involving training middle managers to enact PSC in work units and reduce job stressors. Event 2 was the shock COVID-19 pandemic which occurred midintervention (at 4 months). Three waves (T1, 0 months; T2, 4 months; T3, 8 months) of data were collected from experimental (295T1, 224T2, 119T3) and control (236T1, 138T2, 83T3) employees across 22 work groups. Multilevel analysis showed in Event 1 (T1T2) a significant Group × Time effect where PSC (particularly management priority) significantly increased in the experimental versus control group. Under Event 2 (T2T3), PSC was maintained at higher levels in the experimental versus control group but both groups reported significantly increased PSC communication and commitment. Results suggest that middle management training increases PSC within 4 months. Event 2, COVID-19 was shocking and its novelty, disruption, criticality, and timing in Australian industrial history enabled a strong top management response, positively affecting the control group. PSC may be sustained and built in times of shock with top management will, the application of PSC principles, and a top-level pro-psychological health agenda. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".