The Internet and access to evidence: how are nurses positioned?
Bibliographic record
Abstract
BACKGROUND: Published literature that describes the use of the Internet by nurses is scant, but it does reveal that there has been a delay in the acceptance of the Internet as a workplace tool by the medical community and, in particular by nurses. AIMS: The purpose of this article is to report on a study of how often and from what location nurses accessed the Internet, as well as the types of information they were seeking. In addition, our goal was to compare nurses' Internet use with that of physicians and the public at large, and to highlight structural and institutional challenges to nurses' use. METHODS: Surveys (1996 and 1998) of Alberta Registered Nurses were used to examine their use of technology at work and at home. Additional data sources were used to compare nurses to physicians and to the general public. RESULTS: While nurses' Internet and e-mail use at home increased over the 2-year period and was comparable with other groups, Internet use at work was low compared with other groups despite adequate workplace access. CONCLUSIONS: Nurses are more likely to value interpersonal contact, and prefer to use personal experience and communication with colleagues and patients rather than on-line and traditional sources of practice knowledge. In order for an information source to be seen as valuable in the clinical setting, contextually relevant information needs to be accessed quickly and efficiently. Energies should be focused on constructing information systems that address the particular needs of nurses.
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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 | 0.030 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".