Unleashing the Creativity and Innovation of Our Greatest Resource—The Governmental Public Health Workforce
Bibliographic record
Abstract
CONTEXT: Creativity and innovation in the governmental public health workforce will be required to generate new ideas to solve complex problems that extend beyond traditional public health functions such as disease surveillance and monitoring. Creativity and innovation can promote and advance necessary organizational transformation as well as improve organizational culture and workplace environment by motivating employees intrinsically. However, there is little empirical evidence on how rewarding creativity and innovation in governmental public health departments is associated with organizational culture and workplace environments. OBJECTIVE: This study describes (1) the degree to which creativity and innovation are rewarded in governmental public health agencies and (2) associations between rewarding creativity and innovation and worker satisfaction, intent to leave, and workplace characteristics. DESIGN: The cross-sectional Public Health Workforce Interests and Needs Survey (PH WINS) was administered using a Web-based platform in fall 2017. SETTINGS AND PARTICIPANTS: Data used for these analyses were drawn from the 2017 PH WINS of governmental health department employees. This included state health agency and local health department staff. PH WINS included responses from 47 604 staff members, which reflected a 48% overall response rate. PH WINS excludes local health departments with fewer than 25 staff or serving fewer than 25 000 people. RESULTS: Fewer than half of all workers, regardless of demographic group and work setting, reported that creativity and innovation were rewarded in their workplace. Most measures of worker satisfaction and workplace environment were significantly more positive for those who reported that creativity and innovation were rewarded in their workplace. CONCLUSION: This research suggests that promoting creativity and innovation in governmental public health agencies not only could help lead the transformation of governmental public health agencies but could also improve worker satisfaction and the workplace environment in governmental public health agencies.
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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 | 0.040 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".