National health policy 2017: Can it lead to achievement of sustainable development goals?
Bibliographic record
Abstract
Sustainable Development Goals (SDGs) benefited through the valuable lessons learned from Millennium Development Goals (MDGs) by forwarding the unfinished agenda of MDGs from 2015 onwards. India has also made tremendous improvements during implementations in concurrence with the MDGs, yet lacks in various fields. In 2017 National Health Policy (NHP 2017) was launched by Government of India (GOI) replacing National Health Policy 2002. There is much positive step forward in NHP 2017, but in combination with inadequacies regarding approaches to many key objectives necessary policy corollaries are much needed; otherwise many goals will remain unattainable. This new health policy will help keep improving the maternal and child health but still is unspecific about ‘Health as a fundamental right’ and many burning issues including, the emerging problems of non communicable diseases (NCD), violence on women, sanitation and most importantly ignoring the dire need of long term financial vision of primary health care as well as public health sector in the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".