A Systematic Review of the Cost-Effectiveness of Nurse Practitioners and Clinical Nurse Specialists: What Is the Quality of the Evidence?
Bibliographic record
Abstract
Background. Improved quality of care and control of healthcare costs are important factors influencing decisions to implement nurse practitioner (NP) and clinical nurse specialist (CNS) roles. Objective. To assess the quality of randomized controlled trials (RCTs) evaluating NP and CNS cost-effectiveness (defined broadly to also include studies measuring health resource utilization). Design. Systematic review of RCTs of NP and CNS cost-effectiveness reported between 1980 and July 2012. Results. 4,397 unique records were reviewed. We included 43 RCTs in six groupings, NP-outpatient (n = 11), NP-transition (n = 5), NP-inpatient (n = 2), CNS-outpatient (n = 11), CNS-transition (n = 13), and CNS-inpatient (n = 1). Internal validity was assessed using the Cochrane risk of bias tool; 18 (42%) studies were at low, 17 (39%) were at moderate, and eight (19%) at high risk of bias. Few studies included detailed descriptions of the education, experience, or role of the NPs or CNSs, affecting external validity. Conclusions. We identified 43 RCTs evaluating the cost-effectiveness of NPs and CNSs using criteria that meet current definitions of the roles. Almost half the RCTs were at low risk of bias. Incomplete reporting of study methods and lack of details about NP or CNS education, experience, and role create challenges in consolidating the evidence of the cost-effectiveness of these roles.
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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 | 0.072 | 0.356 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.015 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".