Understanding the role of networks and network actors in the implementation of practice change innovations in Ontario's long-term care homes
Notice bibliographique
Résumé
The challenges associated with practice change and the introduction of new knowledge or innovations in health care has been widely studied (1-4). These challenges often relate to the design and planning of improvement interventions, organizational and institutional contexts, leadership, and sustainability and spread beyond the initial intervention period (5). Many of these are relational challenges associated with the interactions amongst decision-makers who identify the need for knowledge, the individuals who develop the new knowledge, and the individuals who are then charged with implementing and/or tracking the impact of the new knowledge (6). Since these roles are often fulfilled by different people, the relationships amongst key role holders are thought to be critical to making practice change initiatives work. Social network theory centres on the role of relationships in the creation, spread, and utilization of knowledge (7, 8). In this dissertation, the roles and relationships between boundary spanners and opinion leaders in long-term care (LTC) was examined. LTC was the epicentre of the COVID-19 crisis in Ontario where at least 547 LTC facilities have suffered an outbreak since the start of the pandemic (9). Long-term care (LTC) is an understudied sector where quality of care has historically been a concern and organizations face their own unique challenges in moving research into practice (10, 11). This dissertation focuses on understanding the roles of intra-organizational network actors, including opinion leaders and boundary spanners, in implementing COVID-19 infection prevention and control (IPAC) guidelines in LTC facilities in Ontario since the start of the pandemic. Boundary spanners are known to be able to connect isolated groupings in large fragmented systems whereas an opinion leader is an individual who is able to carry information across social boundaries between groups (12, 13). It was hypothesized that these network actors have high “translation capability” or “translation competence” which is defined as “the ability to translate an idea from one context to a practice in another context” (14, 15). My research addressed the following research questions: (1) How do intra-organizational social networks in LTC facilities influence the implementation of new knowledge about care in Canada? (2) How do network actors belonging to intra-organizational social networks in Canadian LTC facilities influence the implementation of the new COVID-19 IPAC guidelines? (3) How does translation competence impact the implementation of the COVID-19 IPAC guidelines? The dissertation is comprised of three related studies. Study 1 addressed research question 1: a systematic scoping review of existing literature focusing on the role of network actors in the implementation of new knowledge was conducted. Study 2 addressed research question 2: a quantitative social network analysis was conducted to outline how existing networks are organized and aid in the identification of network actors in 8 LTC homes from different parts of Ontario, spanning various sizes and types of ownership. Study 3 addressed research questions 2 and 3: semi-structured interviews were conducted with the identified network actors to elicit their experiences with the evolving COVID-19 IPAC guidelines, their readiness and potential determinants to implementing the guidelines as well as their perceived role within their social networks.
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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 | 0,007 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,008 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,002 | 0,006 |
| 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 ».