Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div> 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 “<i>always</i>” or “<i>usually</i>”, 69% of HCWs “<i>definitely would</i>” 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 “<i>connected</i>” or “<i>very connected</i>” 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. </div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.815 | 0.507 |
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 it