Knowledge translation and the Public Health Inspector
Bibliographic record
Abstract
Background: Knowledge translation (KT) is the process of using the best available knowledge to inform decision-making. Public Health Inspectors (PHIs) are tasked with the critical responsibility of protecting public health. However, there is little data available about how effective and consistent current methods of distributing information to professionals across Canada are. The efficacy of KT has implications on the PHI profession and ultimately, public health protection. Objective: The purpose of this research is to identify how PHIs across Canada take evidence and incorporate it into practice. Methods: A survey was created with questions focused on determining what information PHIs use when making public health decisions, how PHIs go about finding the information required, and the level of trust invested into each source of data. Questions were formulated with guidance from the National Collaborating Centre of Environmental Health (NCCEH). It was distributed electronically to PHIs via social media and BCIT. Results: PHIs use evidence-based information to advise their decisions and actions always (43%) or often (46%) in daily practice. Government agencies, professional organizations, peer-reviewed literature, and colleagues are most often used and deemed as reliable resources. Although very frequently used, the internet was seen as neither reliable nor unreliable. 77% of respondents cited that barriers exist that impede their access to evidence-based information. The most common barriers listed were time constraints, costs, and lack of relevant information. Conclusions: The internet is becoming an increasingly popular means by which knowledge is delivered. However, web-based public health resources need to be more concise, easily accessible, PHI-specific and facilitated by reliable entities to effectively address barriers to practice. Increased communication of evidence, practices, and standards are required between health authorities, government agencies, and PHI professionals to ensure consistent and cohesive protection of public health.
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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 | 0.088 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.026 | 0.012 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".