An international comparison of computer networks’ use and potential use
Bibliographic record
Abstract
This paper explores nursing organizations’ experiences, views and policies on computer networks; their ability to access such networks; and to what uses nursing organizations can put the Internet and other networks. There were significant differences between poor and rich countries with respect to access to networking facilities, but not with respect to opinions of the use or potential use of networking. Roughly two-fifths of nursing organizations had access to either the Internet or a local area network (LAN). Richer countries were more likely to have access to both the Internet and LANs. About a half of organizations had email, but only about a quarter accessed email lists. About two-fifths used the World Wide Web (WWW) but only about a tenth had access to USENET newsgroups. There was a significant difference between rich and poor countries with respect to WWW and USENET, with richer countries having greater access. Training for nurses and policies for using computer networks were identified in few organizations, although the potential for computer networks was understood by most. Enthusiasm for using computer networks was particularly noted in poorer and geographically more remote countries. From a list of ten services available via the Internet, the network resource most valued by nursing organizations was online databases; the least valued was videoconferencing.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".