Tightening Up the Nomenclature for Non-Physician Clinicians: Why Not Call All of Them Physician Assistants?
Bibliographic record
Abstract
of physicians and non-physician clinicians in rural primary health centers in India,’ ’ published in the November 2013 edition of Global Health: Science and Practice (GHSP).1 The authors have to be lauded for their efforts to produce evidence on patient perceptions of care provided by non-physician clinicians (NPCs)— an issue that receives little attention, as most of the focus is on the technical quality of care provided by such clinicians. Debates have been ongoing about the best course of action tomeet the health care needs of rural populations in India.2,3 Given that the Indian health care sector is a vast system with diverse needs and attendant chal-lenges, it is reassuring to know that there are potential viable alternatives to allopathic doctors in areas with
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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 | 0.016 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".