Development of Healthcare in Verkhotomsk Volost and Shcheglovsk in Late 19th — First Quarter of 20th Centuries
Bibliographic record
Abstract
The article is devoted to problem of organization of practice of medical care in Kemerovo (Ust-Iskitimskoye village of Verkhotomsk volost of the Kuznetsk district of Tomsk province until 1918, Shcheglovsk city in 1918-1932) in the late 19 th - first quarter of 20 th centuries. The research urgency is caused by studying the practice of formation of organizational structure of health care at low territorial levels. The novelty of the research is seen in the statement of the problem and its historiographical solution on the example of Kemerovo. The author considers peculiarities of the organization of the work of the rural medical station in the volost village Ust-Iskitimskoye, practice of rural medicine and uphold of the principles of the Soviet system of health care in Shcheglovsk. The specified period is proposed to suggest the initial stage in the organization of urban health. The essential characteristics of this stage are determined. Special attention is paid to the establishing medical personnel. Biographical background is given. The collected empirical material allows to eliminate inaccuracies and errors in coverage problems available in the regional literature. The author believes that the presented results are applicable for further study of the history of health service of Kuzbass, and in the didactic purposes for the formation of a civic position of a future medical worker.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".