Bibliographic record
Abstract
The diploma thesis deals with the topic of homes for the elderly during the covid-19 pandemic. Specifically, it deals with the period of time from year 2020 untill the first quarter of the year 2022, where in said period the homes for the elderly were forced to adapt to the new situation of the covid-19 pandemic. The thesis aims to map the situation of the homes for the elderly in the covid-19 pandemic, with focus on Czech lands, and then create a strategy for dealing with pandemic inside the homes for the elderly. To achieve this, the author collected information regarding measures existing inside the homes, also focusing on their relevancy and efficiency, then the said information was utilised to evaluate existing measures. From the collection of information author creates a strategy, which could be used to combat future pandemics of either similar or same types and this strategy would then help homes for the elderly to overcome said situations better. To gather all the information needed, author used multiple sources of literature, foreign sources, legislatives, annual reports, and interviews carried out inside the home for the elderly Sue Ryder. The thesis, aside from the aforementioned strategy, also maps all government regulations relevant for the homes for the elderly during the pandemic,...
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".