Continuing Education in Informatics Among Registered Nurses in the United States in 2000
Bibliographic record
Abstract
BACKGROUND: Continuing education is one method for acquiring competency in informatics among nurses. However, little is known about nurses' participation in continuing education in informatics and the factors or characteristics that motivate them to pursue this type of education. This article identifies the proportion and characteristics of U.S. registered nurses reporting continuing education in informatics in 2000. METHODS: A secondary data analysis was conducted with data from the National Sample Survey of Registered Nurses. More than 25,000 nurses responded to this survey in 2000. Bivariate and logistic regression analyses were conducted to examine the association of reported continuing education in informatics with demographic, educational, and other characteristics of respondents to the survey. RESULTS: Of the respondents, 21% reported continuing education in informatics in the year before the survey. The probability of continuing education in informatics increased with Internet access and decreased for nurses working outside hospitals or providing direct patient care. CONCLUSION: Relatively low proportions of registered nurses report continuing education in informatics, but some opportunities exist to increase rates.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".