How psychosocial safety climate (PSC) gets stronger over time: A first look at leadership and climate strength.
Bibliographic record
Abstract
Psychosocial safety climate (PSC) reflects the priority an organization sets for the psychological health and safety of its employees, important to predict future job design and worker health. PSC is assessed by aggregating employee perceptions to determine PSC level (mean scores) and strength (converging perceptions). Theoretically, the ideal climate is when PSC is high and strong, yet we do not know how to build these fundamentals. Since team leaders may transmit and shape PSC as set down by senior management, we explore their role (i.e., PSC and transformational leadership) in increasing and converging PSC perceptions in a team. We used three-wave longitudinal data (6-month lags) from 49 team leaders and 281 Australian health care workers nested in 49 teams. Multilevel analysis showed that team PSC levels increased over time. Using the consensus emergence model, PSC strength was moderated by PSC leadership. Considering PSC starting levels, when low, high PSC leaders were associated with increasing PSC, but if starting levels were high, low PSC leaders were associated with decreasing PSC levels and strength while high PSC leaders were associated with sustaining PSC strength. Transformational leaders had smaller effects than PSC leaders on PSC levels and no effect on strength. Mid-leaders' values and actions for employee psychological health are important to build PSC level and sustain strength. In this, they can reinforce PSC certainty and its safety signal effect which is shown to be important for reducing psychological problems. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".