Bibliographic record
Abstract
Introduction A number of previous studies have collected data on a hospital’s use of databases and the librarian’s role in the process. These studies express common themes and suggest activities for librarians wishing to promote the use of new technologies. The first theme: While it seems clear that some physicians are competent and satisfied users of new computer search systems, many more, unfortunately, are unaware of the potential time saving features and powerful search capabilities of their search systems. Health sciences librarians have been advocating the use of indexes and abstracts for as long as these products have been available. More than twenty years ago, the National Library of Medicine pioneered online access to the literature with the introduction of Medlars online (Medline). Medline initially consisted of a subset of 236 of the top medical journals indexed in Index Medicus and was viewed as an interesting supplement to manual searching; it now is used routinely as the preferred method of access by thousands of Librarians and health care professionals. Some faculty, though, still rely on the traditional methods of asking a colleague, scanning a personal copy of a journal and, of course, going to the library. Traditionally, CD-ROM systems were only available in the library and doctors and librarians met each other there to discuss problems for searching. The results of a Canadian survey indicated that physicians in Ontario made little use of Libraries because they had no time to search for information beyond that they could obtain quickly from colleagues or from reference material in their own collections. Other studies found that the primary reason of a clear preference for hospital libraries, either medical school or medical society libraries where information was used for both clinical and research purposes, was that the library was the most important place of locating printed sources on which doctors still rely for browsing the literature. Over the past few decades, the role of the medical librarian has become increasingly complex, due to the explosion of information, and the way information is now digitized, libraries are increasingly virtual. Now the additional problem is that clinicians need information but not any information. They need evidence from high quality research. The information is available, but they may have not time to search effectively. To meet their needs, the librarian must adopt the role of going out of the library to meet the clinicians, themselves.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".