Activities of “flying” medical teams in the Arkhangelsk province (end of XIX century – 1920)
Bibliographic record
Abstract
The article presents a historical example of solving the issue of providing public health care in hard-to-reach and sparsely populated territories in the context of a lack of medical personnel and the danger of outbreaks of epidemic diseases. The data concerns mobile medical units, specifically their management experience and recruiting in the Far North of the European part of Russia. Particular attention is paid to little-known facts relating to the last quarter of the XIX century and the First World War. In the Arkhangelsk province, the successful experience of “flying” medical teams, tested in the conditions of warfare, began to be applied in peacetime for temporary workers at the time of fishing and hunting on the remote deserted coasts of the northern seas. Due to the expansion of social infrastructure, the need for “medical units” disappeared, but was again requested in 1916-1920, when, in conditions of war and the mobilisation of medical personnel in the army, the civilian population of remote districts did not receive sufficient medical care, which created the danger of massive epidemics. A feature of this period was the widespread involvement of women doctors in the mobile medical units, even before the officially adopted resolution on their mobilisation in wartime. The activities of mobile medical units allowed reducing the risk of epidemics by localising individual cases and preventing them from spreading among the population, which in war conditions could lead to a quarantine of the Arkhangelsk port and paralyse economic and military relations with allies. Mobile anti-epidemic units became widespread in the following decades (especially during the years of the Civil and Great Patriotic / World War II). They were and continue to be of particular importance for assisting the population of remote northern territories, primarily leading a nomadic lifestyle. The article is based on analysing a large number of original archival sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".