Historical Developments in Ensuring Education for Children undergoing Medical Treatment : Focusing on Developments in English-speaking Countries
Bibliographic record
Abstract
In this article, in order to obtain clues for the historical verification of education for sick children, the historical development of such education are reviewed to obtain a historical perspective on the guaranteeing education for children undergoing medical treatment, focusing mainly on English- speaking countries. The reason for focusing on developments in English-speaking countries in this paper is that it may be useful to note the development of the so-called “new history of medicine.” In the UK, it is confirmed that educational guarantees for sick children were institutionalized from an early stage along with the development of childrenʼs hospitals. In the US, it is also confirmed that educational guarantees for children who require medical care has been established at the federal level, and that the scope and scope of the guarantees has been expanded, sometimes accompanied by litigation. In Canada, it is confirmed that the development of the Toronto Childrenʼs Hospital, based on previous research is a noteworthy example. The impact and influence of COVID-19 in the recent past is also outlined. It is considered necessary for children undergoing medical treatment to learn in a special environment due to their health and medical constraints. Discussions on inclusive education have become active, and the goal is for all children to learn in the same place. On the other hand, children undergoing medical treatment must be treated as an exception. If that is so, then the need to maintain and develop a system that continues learning in parallel with treatment should be universally explored. It is necessary to continue to consider this question.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| 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".