N95 Respirators for a Diverse Population of Healthcare Workers: A Mixed-Methods, Pilot and Feasibility Study
Notice bibliographique
Résumé
Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a global effect. While most of the transmission occurs through droplets produced by an infected individual, SARS-CoV-2 can be transmitted through virus-containing aerosols. The use of N95 respirators reduces the risk of infection; however, in the absence of standardized testing facilities, as well as supply chain and border challenges, Canadian healthcare workers (HCW) had to rely on United States (US) standards and respirators. In Canada, women represent 82% of HCWs, but most masks and respirators have been designed based on the anthropometrics of average men in the US and Europe. In the absence of a tight seal, female HCWs and any individual who does not fit the average male head and face, including individuals of different ethnicities, are at risk of contracting SARS-CoV-2 and other infectious diseases. Thus, there is a knowledge gap on the effects of gender and ethnicity on the fit of N95 respirators and the implications of poor fit on the physical and psychological well-being of HCWs. Objectives: Primary: Feasibility of a multi-center mixed-method study, with a sample size of 100, 50% of participants self-identifying as non-white and having at least 1 characteristic of interest. Secondary: (1) Generate quantitative evidence on N95 fit using a PortaCount fit test, (2) describe participant-reported feelings on fit and breathability, and (3) evaluate the impacts of the pandemic and limited supply of N95’s on a HCWs overall physical and mental well-being. Methods: This study was a mixed-method prospective pilot and feasibility study consisting of (1) a quantitative fit test and (2) a qualitative survey on N95 fit and comfort, as perceived by HCWs. The quantitative fit was assessed using a TSI PortaCount test and facial measurements of bizygomatic breadth and Menton-Sellion length. In parallel, a survey was administered to collect sociodemographic information, gauge the HCW's assessment of N95 fit and comfort, and assess the impact of PPE- related challenges on the physical and mental well-being of HCWs. Analysis: Primary: The sample size, the proportion of various HCWs, and the number of participants who completed both aspects of the study were reported using descriptive statistics. Secondary: The results of the quantitative fit test, as well as the domains assessed in the survey using Likert scales, were summarized using descriptive statistics. Additional patient-reported assessments were collated and presented to provide a comprehensive reflection of HCW's feelings and attitudes on respirator fit and comfort. Results: Following a study amendment to increase eligible sites, 37 of the 41 (90.2%) approached HCWs consented to participate, 36 of the 41 (97.3%) were successfully fitted, and all 36 HCWs completed the survey. Compared to the other included HCWs, female HCWs who identified as non-White had the lowest mean fit factor. Differences in Menton-sellion length and bizygomatic breadth were also observed between both male and female HCWs and between white and non-White HCWs. These results were corroborated by the survey data. On average, female HCWs reported lower scores in all measured domains, and the majority of HCWs reported physical discomfort, including headaches and itching, and negative impacts on their psychological well-being, as a result of fit, availability, and prolonged use of N95. Conclusion: Despite the challenges of conducting research in the context of the COVID-19 pandemic, we have identified gender and ethnicity as key factors in the fit of N95 respirators and the negative implications of existing respirator designs on the physical and psychological well-being of HCWs. Future studies, including a larger mixed-method study, and respirator designs should consider the effects of gender and ethnicity to ensure that they reflect the diverse demographic of HCWs.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,019 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».