The 3 C’s of Consideration for COVID-19 Workplace Fever Detection Device Selection: Context, Calibration & Cost
Bibliographic record
Abstract
COVID-19 screening protocols have become normal practice for employees entering workplaces around the world. However, workplace screening programs that include temperature detection via infrared thermometers or thermal detection cameras often violate many technical specifications for the correct use of these devices. Therefore, this article aims to provide practical guidance for non-thermal imaging specialists responsible for selecting thermal detection devices for workplace screening protocols. Focusing on three critical points of consideration, including the context of use, calibration of equipment, and cost of purchase and maintenance, readers are presented with a framework to guide their decision-making. This framework not only prioritizes the health and wellbeing of employees by ensuring the context of use is appropriate but balances the cost of calibration, purchasing and additional supporting supplies. Further, the presented framework extends beyond the COVID-19 pandemic and can be easily adapted to implement any new workplace technology.
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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 | 0.023 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".