Evaluation of Nurse‐Led and Student‐Led Community‐Based Clinics: A Scoping Review
Bibliographic record
Abstract
AIM: To synthesize approaches used to evaluate nurse-led clinics (NLCs) and student-led clinics (SLCs) delivering community-based primary healthcare. DESIGN: A scoping review based on Joanna Briggs Institute (JBI) guidelines. METHODS: This review included articles evaluating the impact of NLCs and SLCs, published between 2013 and 2023. The Quadruple Aim Framework for health systems quality improvement was a reference point for thematic analysis. DATA SOURCES: CINAHL Complete, ProQuest Nursing & Allied Health, PubMed, Scopus, Health Systems Evidence, Ovid Emcare and grey literature repositories were searched in March-June 2023. RESULTS: Our search yielded a total of 891 articles and 43 articles were included in this scoping review. Diverse quantitative and qualitative methods and concepts of interest were evident in the evaluations of NLCs (n = 15), medical SLCs (n = 15) and interprofessional SLCs (n = 13). Extracted data spoke to the evaluation of either client experience, health of communities, systems of care delivery or provider experience, with systems of care delivery being the most consistently evaluated domain across all clinic types. CONCLUSION: Traditional and non-traditional evaluation measures spanning the Quadruple Aim Framework were used to study community-based NLCs and SLCs. Opportunities remain for broadening the range of indicators and methods used to capture clinic impact on health equity. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Numerous transferable research approaches are available to students and clinical professionals for supporting the design and iterative improvement of innovative primary healthcare clinics. IMPACT: The results highlight ways in which NLCs and SLCs may be evaluated for their concurrent impact on healthcare service delivery and clinical education systems. REPORTING METHOD: PRISMA-ScR. PATIENT OR PUBLIC CONTRIBUTION: Feedback amassed during presentations to nursing audiences informed the enclosed discussion points. TRIAL REGISTRATION: Review protocol was published with the Open Science Framework under ID 10.17605/OSF.IO/FP6S4.
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 | 0.095 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".