USING INTER-ORGANIZATIONAL NETWORK ANALYSIS FOR QUALITY IMPROVEMENT 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 quantitative analysis of the structure and determinants of inter-organizational advice networks in the sector, and of how these networks compare with interpersonal advice networks. 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, and LTC facilities that were seen as models for quality care. We used exponential random graph modeling and quadratic assignment procedure correlation analysis to analyze data from 482 respondents (RR, 52%). Compared with interpersonal advice networks, the inter-organizational networks were more dense and interconnected and featured more relationships that spanned provincial boundaries. As in the interpersonal networks, opinion leading and boundary spanning LTC facilities were identified in all provinces and regions, but opinion leadership in the inter-organizational networks was more centralized around a smaller number of facilities recognized as exemplars of quality residential care. These differences between the two types of advice networks suggest the value of understanding and utilizing both to disseminate best practices throughout the sector.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".