Unleashing the Creativity and Innovation of Our Greatest Resource—The Governmental Public Health Workforce
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,040 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».