Necessary Products for the Prevention and Treatment of Pressure Injuries: Lessons Learned That Translate Beyond the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: To identify the challenges encountered in obtaining the required support surfaces and products to meet pressure injury (PrI) prevention and treatment needs during COVID-19. METHODS: The authors used SurveyMonkey to gather data on healthcare perceptions and the challenges experienced regarding specific product categories deemed necessary for PrI prevention and treatment in US acute care settings during the pandemic. They created three anonymous surveys for the target populations of supply chain personnel and healthcare workers. The surveys addressed healthcare workers' perceptions, product requests, and the ability to fulfill product requests and meet facility protocols without substitution in the categories of support surfaces and skin and wound care supplies. RESULTS: Respondents answered one of the three surveys for a total sample of 174 respondents. Despite specific instructions, nurses responded to the surveys designed for supply chain personnel. Their responses and comments were interesting and capture their perspectives and insights. Three themes emerged from the responses and general comments: (1) expectations differed between supply chain staff and nurses for what was required for PrI prevention and treatment; (2) inappropriate substitution with or without proper staff education occurred; and (3) preparedness. CONCLUSIONS: It is important to identify experiences and challenges in the acquisition and availability of appropriate equipment and products for PrI prevention and treatment. To foster ideal PrI prevention and treatment outcomes, a proactive approach is required to face daily issues or the next crisis.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".