3D-Printed Personal Protective Face Shields During the COVID-19 Pandemic: A Survey of Canadian Frontline Workers
Bibliographic record
Abstract
Background During the coronavirus disease 2019 pandemic, three-dimensional (3D) printing was utilized to rapidly produce face shields for frontline workers in response to an acute shortage of personal protective equipment (PPE). In this study, we examine the perceived utility and performance of 3D-printed (3DP) face shields through a survey of frontline workers in Ontario, Canada. Methodology Frontline workers who received community-produced 3DP face shields from the Canadian initiative "3DPPE GTHA" (March-December 2020) were invited to participate in the study. The survey response rate was 54.3%. Of 63 respondents, 39 were patient-facing and 24 were community-facing frontline workers. Participants were asked to rate performance measures in 10 categories on a five-point Likert scale. Data were categorized by organization and frontline worker type, and a t-test was used to determine statistically significant differences among subgroups. Results The mean preference for 3DP face shields among respondents was 3.2 out of 5 (95% confidence interval [CI]: 2.1-4.3). Community-facing respondents reported significantly greater overall utility scores for 3DP face shields (3.58, 95% CI: 3.38-3.79) compared to respondents working in a patient-facing profession (2.95, 95% CI: 2.77-3.13; p < 0.05). However, no differences were reported in portability and compatibility with other PPE. Respondents from organizations with large service volumes reported significantly lower overall utility scores (2.67, 95% CI: 2.44-2.89) than respondents in organizations with smaller service volumes (3.45, 95% CI: 3.28-3.62; p < 0.05). Conclusions Community-facing frontline workers and those from smaller service volume organizations endorse higher utility for 3DP face shields than patient-facing frontline workers. Despite this, frontline workers generally rate 3DP face shields positively. 3DP face shields are a viable option for personal and community use and can be used to supplement supply in a community setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".