Supporting professionals in the use of N95 masks at the start of a COVID-19 pandemic: a quality improvement approach
Bibliographic record
Abstract
Abstract Background: At the beginning of the COVID-19 pandemic, healthcare professionals (HCP) faced many clinical uncertainties, due in part to the rapid evolution of knowledge about this disease and how to adequately protect themselves. The impact of a workshop alone on improving healthcare professionals’ (HCP) knowledge of the proper use of N95 masks at the beginning of the COVID-19 pandemic was unknown. Objective:We aimed to describe the development and implementation of a workshop on the proper use of N95 masks in hospital and its impact on HCP knowledge. Design: Quality improvement approach using a mixed-method, pre/post workshop design, based on the ADDIE instructional design framework. Setting and participants: All HCP working in one hospital in an urban region (Laval, Canada) in April 2020 were eligible. Intervention: Workshop content based on recommendations and procedures available at that time and validated by hospital microbiologists. Main outcome measures: We assessed participants’ knowledge on using N95 masks by questionnaires, pre and post workshop. Results: We elaborated the workshop content on and it was offered to HCP within one month. Of the 150 HCP who attended the 18 workshops, 69 completed the pre- and post-questionnaires. Most were women (88%) and nurses (59%). Participants’ knowledge increased after the workshop (24-85%) and their anxiety was subjectively reduced. Conclusion: Using a workshop to share the latest recommendations on the proper use of N95 masks increased HCPs’ confidence. The quality improvement approach allowed the flexibility and speed of action required in an urgent sanitary context.
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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 | 0.094 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".