Marginal Shortening of Life Expectancy in Japan During COVID-19: A Low Pandemic Impact Country Due to Improved Health Infrastructure and Awareness
Bibliographic record
Abstract
The novel coronavirus (SARS-CoV-2) caused the COVID-19 pandemic, which led to a large number of deaths worldwide, particularly in rich and developed countries, thereby decreasing the average life expectancy (ALE) or average lifespan (ALS) of the people living in these countries. We investigated the pandemic’s effect on the ALE of the Japanese male and female population. Japan’s declining ALE year-over-year was compared with high-ranking LE countries. For both genders, Japan’s ALE increased every year until 2020, even though 2020 was a pandemic year. A small decrease due to the pandemic could not reduce Japan’s overall LE in 2020. In 2021 and 2022, Japan’s overall LE decreased, but once the pandemic ended in 2023, it returned to its pre pandemic trend of increasing. When considering both genders among the high LE and rich and democratic countries such as Hong Kong, Switzerland, Singapore, Sweden, Norway, Italy, South Korea, and Spain, Japan’s ALE was least affected. Due to its improved health infrastructure (% GDP spending on healthcare) and public awareness about the pandemic (mask usage), Japan remained the least affected country during the COVID-19 pandemic. This paper compares the ALE change, % GDP spending on healthcare, and mask usage awareness of the above countries with Japan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".