Participation in the National Collaborating Centre for Methods and Tools’ Knowledge Broker Mentoring Program: a public health inspector perspective
Bibliographic record
Abstract
Although evidence-informed decision making is an important part of the field of public health inspection, finding the time to stay informed of current research can be a challenge amidst day-to-day job expectations. This article will explore how two Public Health Inspectors (PHIs) from Ottawa Public Health, a municipal public health unit in Ontario, incorporated evidence-informed decision making (EIDM) into their work. They built their EIDM skills through participating in the 18-month Knowledge Broker (KB) Mentoring Program offered by the National Collaborating Centre for Methods and Tools. The program required a substantial time commitment, including nine in-person workshop days and dedicated hours to practice research appraisal skills and to complete a rapid review. The inspectors were approved and supported to spend the necessary time; however, they still found it difficult to designate hours for learning while balancing their frontline inspection workload. This article will share observations about the PHI’s involvement, including benefits and challenges as well as factors that facilitated their successful completion of the KB Mentoring Program.
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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 | 0.133 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".