Bibliographic record
Abstract
This article describes the program of research being implemented to investigate the outcomes of certification in the U.S. and Canadian workforce and its relevance to the goal of providing policy makers with pragmatic, action-oriented recommendations. Such recommendations encompass policy issues of workforce production, regulation, distribution, financing, and oversight of credentialing organizations. Few studies exist to substantiate the association between certification credentialing and practice outcomes, thereby leaving policy makers, consumers, and nurses in a vacuum when pushed to protect the public through the credentialing vehicles. The Nursing Credentialing Research Coalition (NCRC) has embarked on a research program that will reveal the relationship between certification and its influence on a nurse’s personal, professional, and practice characteristics as well as those judged by consumers and employers. Selected findings and implications for policy are described. Certified nurses’ reports of early interventions for complications are important quality measures for policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.617 | 0.660 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.013 | 0.104 |
| Scholarly communication | 0.035 | 0.066 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.046 | 0.047 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".