Measures for co-medicals in postwar Okinawa: scarcity of the personnel for allocating vaccinations
Bibliographic record
Abstract
Abstract Background The shortage of doctors in postwar Okinawa has led to the creation of co-medicals. They were called medical service personnel and authorized to handle certain medical treatments. This included tasks such as providing injections and vaccinations under the supervision of the doctor in the health center. Issue/problem The system of co-medicals only applied in Okinawa and not in Japan. As such, when Okinawa was reverted back to Japan in 1972, it was proposed to abolish the system. In the current emergency of the new coronavirus pandemic, we are facing a similar situation in terms of the shortage of medical personnel. Description of the problem In Japan, like many countries, there is a shortage of health resources due to the COVID-19 pandemic. The focus will be on healthcare personnel, those who have taken on the role of COVID-19 vaccinators in Japan, and a comparison of the situation in other countries. Results In Japan, vaccinations are legally limited to being administered by doctors and nurses. However, pharmacists have taken on the role of administering vaccinations in the UK, US, Ireland, Canada, and Italy, thus COVID-19 vaccinations could be easily integrated into these routine procedures.Even vaccinations by medical students have been carried out. In addition, non-healthcare volunteers have been trained as vaccinators in UK and Italy. The coverage has reached 50% in the UK, 42% in the US, and 30% in Canada, whereas 1.8% in Japan in April (Our World in Data, 2021). Lessons Although the COVID-19 situation is substantially different from the case of postwar Okinawa, it will be necessary to take resilient measures to solve the medical personnel shortage. Additionally, the strict measures of limiting vaccinations by medical doctors may need to be reconsidered in Japan. Establishing a system of allowing co-medicals to have a shared role could offer innumerable benefits for all. Key messages Expanding the task of vaccinations to co-medicals has had beneficial results in several countries. Dividing roles of medical doctor need to be considered.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".