Understanding professional advice networks in long-term care: an outside-inside view of best practice pathways for diffusion
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Interpersonal relationships among professionals drive both the adoption and rejection of consequential innovations. Through relationships, decision-makers learn which colleagues are choosing to adopt innovations, and why. The purpose of our study was to understand how and why long-term care (LTC) leaders in a pan-Canadian interpersonal network provide and seek advice about care improvement innovations, for the eventual dissemination and implementation of these innovations. METHODS: We used a mixed methods approach. An online survey was sent to senior leaders in 958 LTC facilities in 11 Canadian provinces and territories. Participants were asked to name up to three individuals whose advice they most value when considering care improvement and practice innovations. Sociometric analysis revealed the structure of provincial-level advice networks and how those networks were linked. Using sociometric indicators, we purposively selected 39 key network actors to interview to explore the nature of advice relationships. Data were analyzed thematically. RESULTS: In this paper, we report our qualitative findings. We identified four themes from the data. One theme related to characteristics of particular network roles: opinion leaders, advice seekers, and boundary spanners. Opinion leaders and boundary spanners have long tenures in LTC, a broad knowledge of the network, and share an interest in advancing the sector. Advice seekers were similarly committed to LTC; they initially seek and then, over time, exchange advice with opinion leaders and become an important source of information for them. A second theme related to characterizing advice seeking relationships as formal, peer-to-peer, mentoring, or reciprocal. The third and fourth themes described motivations for providing and seeking advice, and the nature of advice given and sought. Advice seekers initially sought information to resolve clinical care problems; however, over time, the nature of advice sought expanded to include operational and strategic queries. Opinion leaders sought to expand their networks and to solicit information from their more established advice seekers that might benefit the network and advance LTC. CONCLUSIONS: New knowledge about the distinct roles that different network actors play vis-a-vis one another offers healthcare professionals, researchers, and decision- and policy-makers insights that are useful when formulating best practice dissemination strategies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it