The Effectiveness of Community Nurse Training Programs to Improve Nurses' Competency in Community Settings: a Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
AIM: This study aimed to synthesise the best available evidence on the training methods used in community nurse training programs and examine the effectiveness of such programs on enhancing competency (knowledge, skills and traits) among nurses or nurse practitioners working in community healthcare units. METHODS: A search was conducted on 10 electronic databases to retrieve published and grey literature, specifically randomised controlled trials and clinical control trials. Two reviewers independently screened records, appraised article quality and extracted data from the included studies. Meta-analysis was performed using the RevMan 5.4 software. Effect measures were expressed as standardised mean differences (SMD) and confidence intervals. Subgroup analyses were also performed based on delivery methods. FINDINGS: Fourteen studies were included, 13 of which targeted knowledge as an outcome, 10 focused on nurses' skills and four examined nurses' traits. The analysis revealed that community nurse training programs improved nurses' knowledge via hybrid pedagogical (SMD = 0.15-2.21), online (SMD = 0.12-0.29) and onsite methods (SMD = -0.51 to 0.11) and enhanced their skills through hybrid (SMD = 0.90) and onsite training (SMD = 0.44). Furthermore, the programs improved nurses' traits in psychological empowerment, job productivity, professional development and affect dimensions. CONCLUSIONS: Nursing schools and healthcare institutions may employ diverse pedagogical approaches, including hybrid methods, to achieve positive outcomes for community nurse training programs to improve clinical competencies.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".