Social and organizational factors affecting implementation of evidence-informed practice in a public health department in Ontario: a network modelling approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to develop a statistical model to assess factors associated with information seeking in a Canadian public health department. METHODS: Managers and professional consultants of a public health department serving a large urban population named whom they turned to for help, whom they considered experts in evidence-informed practice, and whom they considered friends. Multilevel regression analysis and exponential random graph modeling were used to predict the formation of information seeking and expertise-recognition connections by personal characteristics of the seeker and source, and the structural attributes of the social networks. RESULTS: The respondents were more likely to recognize the members of the supervisory/administrative division as experts. The extent to which an individual implemented evidence-based practice (EBP) principles in daily practice was a significant predictor of both being an information source and being recognized as expert by peers. Friendship was a significant predictor of both information seeking and expertise-recognition connections. CONCLUSION: The analysis showed a communication network segregated by organizational divisions. Managers were identified frequently as information sources, even though this is not a part of their formal role. Self-perceived implementation of EBP in practice was a significant predictor of being an information source or an expert, implying a positive atmosphere towards implementation of evidence-informed decision making in this public health organization. Results also implied that the perception of accessibility and trust were significant predictors of expertise recognition.
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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 | 0.018 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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