Hospital virtual libraries in Latin America and the Caribbean: A webometric analysis
Bibliographic record
Abstract
Introduction: Information regarding hospital libraries in the United States, Canada and the European Union abounds. Little information however exists in the literature regarding hospital libraries in Latin America and the Caribbean. While new information and communication technologies (ict) are being transferred from developed to less developed countries and major emphasis is being placed worldwide on quality of health care, evidence based-medicine, and the use of information on the decision process mechanisms in the delivery of health care, little is known about the capability and empowerment of hospitals in less developed countries to respond to such needs. Purpose: The purpose of this work is to present the preliminary results of a research in progress on the existing virtual positioning of electronic libraries among hospitals in Latin America and the Caribbean. Method: A webometric analysis was conducted through the electronic search of those Latin American and Caribbean hospital websites hosting a virtual/electronic library available to their user community via the Internet. The study was limited to a search in Google, HotBot and Yahoo, in 2005. BIREME’s Virtual Health Libraries were excluded from the study, considering the available information on the development of this project. Results: A total of 2,523 hospitals were identified, as reported by 34 Latin American and Caribbean countries. However, only 501(19.85%) hospitals reported an institutional website; 56 (11.18%) stated to have a library; and only 17 (3.39%) owned a virtual/electronic library. These countries were the following in descending order: Mexico, Argentina, and Venezuela. Discussion: These preliminary results reflect the need to continue this work of research in order to establish a diagnosis of the existing situation in terms of infrastructure and ict developments so as to improve the access and use of scientific and technical information among hospitals. The paper discusses both, (1) the important role of BIREME’s effort in developing virtual accesses to health libraries in the region and (2) the different implications of this type of research to information providers; end-users; managers and librarians, among others in the health sector.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.068 | 0.104 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".