A SERIES OF STUDIES ON USING SOCIAL NETWORKS TO INFORM AND SUPPORT EVIDENCE-INFORMED PUBLIC HEALTH PRACTICE IN CANADA: INVESTIGATING ORGANIZATIONAL SOCIAL NETWORKS
Notice bibliographique
Résumé
Introduction: In a mixed-methods study I assessed the role of social networks as predictors and outcomes of the implementation of an intervention to promote evidence-informed decision-making (EIDM) in three public health departments in Ontario, Canada. The quantitative strand included the analysis of the role of staff’s position in networks on the adoption of EIDM, the longitudinal evolution of networks, and the association between the name generators’ position in surveys and respondents’ motivation to answer survey questions. The qualitative strand aimed to explain and contextualize the quantitative findings. Methods: A tailored intervention was implemented in the public health departments, including the mentoring of staff through the EIDM process by a knowledge broker. The staff participated in three online surveys before and after the 22-month intervention, providing the names of peers to whom they turned to seek information, whom they considered as experts, and their friends. I assessed the dynamic evolution of social networks, and the role of local opinion leaders (OL) in promoting the adoption of EIDM. I interviewed key network actors about their interpretation and experience regarding the quantitative findings. Results: Overall, there was no statistically significant impact on EIDM behavior and skill in health departments. However, the analysis of the role of OLs in behaviour change showed that non-engaged staff who were connected to highly engaged OLs, and those OLs who communicated with each other improved their EIDM behavior. Social networks became more centralized around already popular staff due to selective training of recognized experts. Highly engaged staff tended to connect to each other, and to limit their connections within organizational divisions over time. In the department where multiple activities were being implemented to support EIDM, the highly engaged staff became more popular due to department-wise presentations and informal information spread. I also found that when name generator questions are asked later in surveys then respondents are more likely to refuse, indicate they do not know anyone, or provide fewer names than when these questions are asked earlier Conclusion: Social network analysis showed the structure of information-seeking relations, the impact of opinion leaders on the EIDM behavior of their peers, and underlying social changes through implementing an EIDM intervention. These findings can inform the design and tailoring of EIDM interventions in public health organizations.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,008 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».