The projects of local governments’ health policy programs in the area of therapeutic rehabilitation reviewed by the Agency for Health Technology Assessment and Tariff System [AOTMiT] in 2010-2017.
Bibliographic record
Abstract
Since 2010, the Agency for Health Technology Assessment and Tariff System (AOTMiT) has issued its opinions on health policy programs (PPZ), including therapeutic rehabilitation. Of 1,813 opinions submitted on the programs, 6.9% (126 opinions) have concerned therapeutic rehabilitation. The purpose of the study is to evaluate the proportion of and determining factors of the negative opinions presented by the AOTMiT. It has been demonstrated that nearly a quarter (23.5% in 2017) of the projects in the field of therapeutic rehabilitation are negatively assessed by the Agency, the most common reason for the negative opinion (72.4%) being inadequate project preparation. The new scheme of PPZ developed by the Agency should make it easier for local governments to design a program, thus significantly reducing the number of negative opinions on therapeutic rehabilitation programs.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".