The impact of nursing on health outcomes of people receiving medication for opioid use disorder: An integrative review
Bibliographic record
Abstract
INTRODUCTION: This integrative review aimed to understand the impact of nursing on the health outcomes of people with opioid use disorder (OUD). METHODS: Whittemore and Knafl's (2005) integrative review methodology guided the review process. The authors searched PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and OVID databases for peer-reviewed, English language articles describing nursing care for people receiving medications for OUD. The literature search followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. RESULTS: A total of 773 articles were identified through the database searches after duplicates were removed; 15 articles met inclusion criteria and were included in the final review from the United States, Canada, Ireland, England, and the Netherlands. Two themes emerged: (1) The effect of nurse-led care on patient outcomes and (2) Nursing roles and the environment of care. Studies emphasized the aspects of nursing care that promote patient-centeredness and patient satisfaction. CONCLUSIONS: The importance of the registered nurse in providing quality care for people receiving medications for OUD is clear from the literature reviewed. The studies in the review highlighted important aspects of the registered nurses' role in affecting outcomes for people receiving medications for OUD including communication and care coordination. CLINICAL RELEVANCE: This review highlights the need for role delineation and further development of nurse-sensitive indicators in the OUD treatment setting.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".