Moving from a reactive to a proactive society: recognizing the role of environmental public health professionals
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted how society is naturally reactive to issues that could have been prevented or minimized in hindsight. Environmental public health professionals (EPHPs) take a proactive approach to health and safety to prevent the occurrence of adverse events that can negatively impact the health of individuals. The Canadian healthcare system largely invests in hospitals and acute care compared to public health. EPHPs have been actively mobilized to assist with the pandemic response and have demonstrated their versatility in skillset—EPHPs have performed a variety of activities, such as enforcement action, education, and contact tracing. Despite EPHPs proving to be a valuable resource during the pandemic, there remains a sense of under-recognition and underappreciation for the work being done. Indeed, the nature of public health work relies on efforts occurring behind-the-scenes—trends over time will reveal the outcomes of health initiatives. Although it is challenging to obtain timely health evidence to justify investment into public health, a continued passive approach to prevention will be harmful to society. Greater acknowledgement and investment of resources into the health protection field can help establish a proactive attitude to thereby lessen the economic and health burdens our communities may face in the future.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".