Certified Registered Nurses: Results of the Study of the Certified Workforce
Bibliographic record
Abstract
Findings show benefits and drawbacks of nursing certification. Overview: During the 30 years that professional certification has been available to nurses, little research has been done to determine its effect. Now that both patients and employers are seeking ways to assess the quality of health care providers, data characterizing the certified nurse workforce is increasingly important. Several small-scale studies have examined various aspects of certification, but until now, we've lacked data on who gets certified and how certification affects those who receive it. This article reports on the findings of stage 3 of the International Study of the Certified Nurse Workforce, the largest study to date on certification, based on a random sample of 19,452 nurses from the registries of 23 certifying organizations in the United States, Canada, and U.S. territories. In this stage of the study, certified nurses' demographic characteristics and the nature of their practice were described, and any benefits or rewards they might attribute to certification were examined. The study's findings show that the typical respondent was a married, Caucasian woman in her late 40s, who had a minimum of a bachelor's degree and worked in a hospital. Seventy-two percent of nurses reported one or more benefits of certification, and almost all respondents reported that certification brought about at least one change in their practice. These results provide initial evidence that certification may give nurses the means or opportunity to practice in a manner likely to improve outcomes. Further research is needed to confirm the certified nurse workforce's contribution to productivity, retention, and high-quality health care.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".