The Soft Underbelly of System Change: The Role of Leadership and Organizational Climate in Turnover during Statewide Behavioral Health Reform.
Bibliographic record
Abstract
This study examined leadership, organizational climate, staff turnover intentions, and voluntary turnover during a large-scale statewide behavioral health system reform. The initial data collection occurred nine months after initiation of the reform with a follow-up round of data collected 18 months later. A self-administered structured assessment was completed by 190 participants (administrators, support staff, providers) employed by 14 agencies. Key variables included leadership, organizational climate, turnover intentions, turnover, and reform-related financial stress ("low" versus "high") experienced by the agencies. Analyses revealed that positive leadership was related to a stronger empowering climate in both high and low stress agencies. However, the association between more positive leadership and lower demoralizing climate was evident only in high stress agencies. For both types of agencies empowering climate was negatively associated with turnover intentions, and demoralizing climate was associated with stronger turnover intentions. Turnover intentions were positively associated with voluntary turnover. Results suggest that strong leadership is particularly important in times of system and organizational change and may reduce poor climate associated with turnover intentions and turnover. Leadership and organizational context should be addressed to retain staff during these periods of systemic change.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".