Personal Protective Equipment Portraits Canada (PPC)–Humanization and surveying mask-wearing nationally
Bibliographic record
Abstract
BACKGROUND: Personal Protective Equipment (PPE) Portraits is a hybridized art and medical intervention that lessens the alienating appearance of PPE through wearable, smiling headshot pictures. During the pandemic, the use of these portraits was expanded, but Canadian initiatives offered portraits only to immediate stakeholders. PPE Portraits Canada (PPC) aimed to provide PPE portraits to any Canadian healthcare institution and surveyed healthcare workers (HCW) regarding these portraits' impact. METHODS: University student volunteers founded PPC via online platforms and coast-to-coast collaborations that allowed any HCW nationwide to request a free portrait via an accessible online form. PPC has gathered feedback from participating HCWs directly via an anonymous and bilingual survey. RESULTS: 70% of HCWs wore their portraits "always" or "usually", 69% of HCWs "definitely would" recommend their portrait, 89.5% of HCWs found that the PPE portraits made a difference in their experiences with patients and 74% found the same for their colleagues. The pre- and post-effect of the portraits, led to a 37.5% greater likelihood that HCWs felt "connected" or "very connected" to patients/residents. For the thematic analysis, 70% or more of the comments were rated as positive, with less than 5% of comments being rated as negative. CONCLUSION: This model's logistical framework can be expanded beyond PPE portraits to other initiatives with limited resources, allowing them to reach and positively impact diverse populations. HCW feedback was predominantly positive. The optimal design and impact of PPE portraits on patients and HCWs should be studied further to improve portrait adoption.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".