Facial Protective Equipment, Personnel, and Pandemics: Impact of the Pandemic (H1N1) 2009 Virus on Personnel and Use of Facial Protective Equipment
Bibliographic record
Abstract
BACKGROUND: Before the emergence of the pandemic (H1N1) 2009 virus, estimates of the stockpiles of facial protective equipment (FPE) and the impact that information had on personnel during a pandemic varied. OBJECTIVE: To describe the impact of H1N1 on FPE use and hospital employee absenteeism. Setting. One tertiary care hospital and 2 community hospitals in the Vancouver Coastal Health (VCH) region, Vancouver, Canada. Patients. All persons with influenza-like illness admitted to the 3 VCH facilities during the period from June 28 through December 19, 2009. METHODS: Data on patients and on FPE use were recorded prospectively. Data on salaried employee absenteeism were recorded during the period from August 1 through December 19, 2009. RESULTS: During the study period, 865 patients with influenza-like illness were admitted to the 3 VCH facilities. Of these patients, 149 (17.2%) had laboratory-confirmed H1N1 influenza infection. The mean duration of hospital stay for these patients was 8.9 days, and the mean duration of intensive care unit stay was 9.2 days. A total of 134,281 masks and 173,145 N95 respirators (hereafter referred to as respirators) were used during the 24-week epidemic, double the weekly use of both items, compared with the previous influenza season. A ratio of 3 masks to 4 respirators was observed. Use of disposable eyewear doubled. Absenteeism mirrored the community epidemiologic curve, with a 260% increase in sick calls at the epidemic peak, compared with the nadir. CONCLUSION: Overall, FPE use more than doubled, compared with the previous influenza season, with respirator use exceeding literature estimates. A significant proportion of FPE resources were used while managing suspected cases. Planners should prepare for at least a doubling in mask and respirator use, and a 3.6-fold increase in staff sick calls.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".