Indexing of Journals in MEDLINE, PubMed, and Other Bibliographic Databases — Why Not CJHP ?
Bibliographic record
Abstract
Pharmacists, other health care professionals, and scientists rely on information in biomedical and other professional journals to keep up to date on advances in pharmacy, medicine, pharmacotherapy, pharmacology, and health care in general and to find answers to specific questions related to patient care, research, education, and related issues. Easy access to information in the literature is important for the provision of optimal patient care. For most of us working in health care, literature search tools, particularly searchable online databases, are the major mode of accessing scientific and medical information. Several publications and searchable databases
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 | 0.022 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.091 | 0.133 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.013 |
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".