The efficacy of PPE for COVID-19-type respiratory illnesses in primary and community care staff
Bibliographic record
Abstract
Background Most cultures believe in ghosts and for the Chinese, the seventh lunar month—the ghost month—causes particular concern. ‘The gates of hell’ are open for the first 14 days of the month which allows the restless ghosts of people who were hungry when they died to haunt the living. In this study, it was hypothesised that if the notion that ghosts are out to harm the living could affect the Chinese, this may be reflected in death statistics. Methods Because the Chinese believe death is more likely during the ghost month, male and female deaths from all causes and from four common causes of death in the first and second fortnights of the seventh lunar months of 1995–2000 were compared in Hong Kong Chinese. Deaths in two consecutive fortnights 30 days before each year9s seventh lunar month were used as controls. Death data were compared using the binomial test with a null hypothesis probability of 0.5 between the first and second fortnights. Results There was no difference in male deaths between the first and second fortnights of the control and seventh lunar months. While there were no significant differences in female deaths during the control month periods, fewer women died overall in the first fortnight of the seventh lunar month (p=0.026). Conclusion To protect their family, the Chinese women postpone death until after the hungry ghosts have been fed and hopefully banished forever.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".