ENHANCING RESIDENT CARE IN NOVA SCOTIA: INNOVATIVE MAPPING OF ADVICE NETWORKS IN LONG TERM CARE
Bibliographic record
Abstract
The purpose of this presentation is to demonstrate how social network analysis can be used to enable the accelerated spread of best practices to improve resident outcomes in residential long term care (LTC).Advice Seeking Networks in Long term Care is a Pan-Canadian study done in partnership with sector leaders (PI: C. Estabrooks).It is the first of its kind to use social network analysis to map advice seeking behaviors among senior leaders in LTC facilities.Drawing on results from the Care and Construction research study, this presentation will demonstrate how mapping these existing networks may facilitate the diffusion of innovations from this study's findings.One senior leader from each nursing home in Nova Scotia was invited to complete an online survey in November 2014.The leaders were asked to identify individuals and organizations from whom they sought advice about improving resident care.Sixty-five percent of senior leaders in Nova Scotia (n=57) responded.The advice seeking patterns were analyzed using Gephi and UCINET, with network maps illustrating the spread and strength of connections.Preliminary analysis of the maps for nursing home leaders in Nova Scotia indicate that networks were contained within provincial geography, with a few important clusters emerging based on corporate structure.Social network analysis can reveal who looks to whom for improving resident care in LTC facilities.At the scale of a province, advice-seeking results offer a valid form of data that can then be used to accelerate the adoption of best practices across LTC facilities.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".