Role of Policy in Best-Practice Dissemination: Informal Professional Advice Networks in Canadian Long-Term Care
Bibliographic record
Abstract
This article examines provincial policy influence on long-term care (LTC) professionals' advice-seeking networks in Canada's Maritime provinces. The effects of facility ownership, geography, and region-specific political landscapes on LTC best-practice dissemination are examined. We used sociometric statistics and network sociograms, calculated from surveys with 169 senior leaders in LTC facilities, to identify advice-seeking network structures and to select 11 follow-up interview participants. Network structures were distinguished by density, sub-group number, opinion leader, and boundary spanner distribution. Network structure was affected by ownership model in Nova Scotia and Prince Edward Island, and by regional geography in New Brunswick. Political instability within each province's LTC system negatively affected network actors' capabilities to enact innovation. Moreover, provincial policy variations influence advice-seeking network structures, facilitating and constraining relationship development and networking. Consequently, local policy context is essential to informing dissemination strategy design or implementation.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".