CHARACTERISTICS OF OPINION LEADERS AND BOUNDARY SPANNERS IN LONG-TERM CARE
Bibliographic record
Abstract
The Advice Seeking Networks in Long Term Care Study used social network analysis to understand the informal advice networks of senior leaders in Canadian long term care (LTC), with the goal of using this knowledge to inform future efforts to more effectively disseminate quality improvement innovations. In this abstract we describe one main component of the study, a qualitative analysis of interviews conducted with 39 opinion leaders, boundary spanners, and advice seekers identified in interpersonal advice networks in the sector. At each of the 958 LTC facilities spanning 11 of Canada’s 13 provinces and territories, we asked one senior leader to complete a survey identifying individuals who were informal sources of advice about quality improvement. The survey data from 482 respondents was then used to identify and interview network advice seekers by their out-degree scores, opinion leaders by their in-degree scores, and boundary spanners by their betweenness centrality scores. Results from thematic analysis of the interviews indicated that advice seekers tend to seek advice from those with whom they deem trustworthy and knowledgeable and with whom they share similar professional backgrounds and care philosophies. Opinion leaders possess an appetite for change and a strong sense of responsibility for improving care throughout the LTC system. They often have career trajectories that move them from clinical to administrative or oversight roles. Advice seeking relationships often endure over many years, transcending roles and even care sectors, and can evolve into reciprocal relationships.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".