A Service-Learning Project Based on a Community-Oriented Intelligent Health Promotion System for Postgraduate Nursing Students: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Service learning (SL) is a pedagogical approach that combines community service with cognitive learning for professionals. Its efficacy in promoting community health has gained broad recognition in nursing education. The application of postgraduate nursing SL programs in community-based intelligent health remains underexplored. Thus, additional investigation is necessary to assess the influence of the SL project based on a community-oriented intelligent health promotion system (SLP-COIHPS) on postgraduate nursing students and health service recipients. OBJECTIVE: This study aims to assess how SLP-COIHPS influences the scientific awareness and research innovation abilities of postgraduate nursing students. In addition, the study sought to examine the experiences of both participating students and health service recipients. METHODS: We conducted a mixed methods investigation by using web-based surveys and conducting interviews. The web-based surveys aimed to explore the differences in scientific awareness and research innovation capabilities between 2 distinct groups: an experimental group of 23 postgraduate nursing students actively participated in SLP-COIHPS, while 23 postgraduate students (matched one-to-one with the experimental group in terms of grade, sex, and research methods) served as control participants. Semistructured interviews were conducted with 65% (15/23) of postgraduate students and 3% (12/405) of community residents who received health services, aiming to assess the project's impact on them. The community-based intelligent health promotion system installed in intelligent health cabins can be conceptualized as an expert system providing valuable references for student health education. It has the capability to generate comprehensive assessments and personalized health guidance plans. Following training, students were involved in offering health assessments, health education, and related services. Subsequently, after the web-based surveys and semistructured interviews, quantitative data were analyzed using the SPSS (IBM Corp) software package, using 2-tailed t tests and Mann-Whitney U tests; qualitative data underwent analysis using the constructivist grounded theory approach. RESULTS: Postgraduate nursing students participating in this program scored 12.83 (Cohen d>0.8; P<.001) and 10.56 (Cohen d>0.8; P=.004) points higher than postgraduate students in the control group in research awareness and research innovation capability, respectively. On the basis of the qualitative results, postgraduate students reported improvement in this program. Analysis of the interviews revealed a total of 12 subcategories across three primary domains: (1) specialized skills, (2) scientific research ability, and (3) comprehensive qualities. Community residents reported high satisfaction and positive experiences. Analysis of the interviews with community residents identified two primary categories: (1) satisfaction and (2) perceived benefits. CONCLUSIONS: SLP-COIHPS had a positive impact on students' development of scientific awareness and research innovation ability. Qualitative study findings also support the further development of practical programs that integrate intelligent health and SL theories in the field of medical education. This includes exploring the potential factors influencing postgraduate nursing students' research capabilities or investigating the long-term effects of the project.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".