Practice Analysis: Adult-Gerontology Acute Care Nurse Practitioner and Clinical Nurse Specialist
Bibliographic record
Abstract
BACKGROUND: Standards for advanced practice registered nurse (APRN) licensure in the United States require certification programs to analyze practice in order to document the knowledge and skills necessary for entry-level adult-gerontology acute care nurse practitioners (AGACNPs) and wellness-through-acute-care clinical nurse specialists (AGCNSs). The practice analysis done every 5 years by the AACN Certification Corporation provides research data for use in establishing test plans for certification of APRNs. OBJECTIVES: To describe the development of a survey to collect information on the current practice of AGACNPs and AGCNSs, and to compare the results from practitioners in the 2 roles. METHODS: In 2016, a task force of subject matter experts created a survey of the practice activities and competencies of AGACNPs and AGCNSs. Respondents rated activities and competencies according to their applicability and significance to APRN practice. The subject matter experts analyzed the ratings to determine which patient care problems, skills and procedures, and competencies would be included in the updated certification test plans. RESULTS: After analyzing the survey responses, subject matter experts retained 135 patient care problems, 45 skills and procedures, and all national competencies for AGACNPs and 123 patient care problems, 56 skills and procedures, and all national competencies for AGCNSs. Both roles involve several of the same patient care problems, skills and procedures, and competencies. CONCLUSIONS: Data from practice analysis surveys formed the basis for developing reliable and valid certification examinations for entry-level APRNs. The information from such studies of practice should inform practicing nurses and students, as well as educators, accreditors, legislators, and regulators, about the work of AGACNPs and AGCNSs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".