The effectiveness and cost‐effectiveness of clinical nurse specialists in outpatient roles: a systematic review
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Increasing numbers of clinical nurse specialists (CNSs) are working in outpatient settings. The objective of this paper is to describe a systematic review of randomized controlled trials (RCTs) evaluating the cost-effectiveness of CNSs delivering outpatient care in alternative or complementary provider roles. METHODS: We searched CINAHL, MEDLINE, EMBASE and seven other electronic databases, 1980 to July 2012 and hand-searched bibliographies and key journals. RCTs that evaluated formally trained CNSs and health system outcomes were included. Study quality was assessed using the Cochrane risk of bias tool and the Quality of Health Economic Studies instrument. We used the Grading of Recommendations Assessment, Development and Evaluation to assess quality of evidence for individual outcomes. RESULTS: Eleven RCTs, four evaluating alternative provider (n = 683 participants) and seven evaluating complementary provider roles (n = 1464 participants), were identified. Results of the alternative provider RCTs (low-to-moderate quality evidence) were fairly consistent across study populations with similar patient outcomes to usual care, some evidence of reduced resource use and costs, and two economic analyses (one fair and one high quality) favouring CNS care. Results of the complementary provider RCTs (low-to-moderate quality evidence) were also fairly consistent across study populations with similar or improved patient outcomes and mostly similar health system outcomes when compared with usual care; however, the economic analyses were weak. CONCLUSIONS: Low-to-moderate quality evidence supports the effectiveness and two fair-to-high quality economic analyses support the cost-effectiveness of outpatient alternative provider CNSs. Low-to-moderate quality evidence supports the effectiveness of outpatient complementary provider CNSs; however, robust economic evaluations are needed to address cost-effectiveness.
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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 | 0.024 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".