Understanding the role of networks and network actors in the implementation of practice change innovations in Ontario's long-term care homes
Bibliographic record
Abstract
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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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".