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Record W4388941148 · doi:10.2196/52279

A Service-Learning Project Based on a Community-Oriented Intelligent Health Promotion System for Postgraduate Nursing Students: Mixed Methods Study

2023· article· en· W4388941148 on OpenAlexvenueno aff
Ting Sun, Xuejie Xu, Ningning Zhu, Jing Zhang, Zuchang Ma, Hui Xie

Bibliographic record

VenueJMIR Medical Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
FundersDivision of Graduate EducationAnhui Provincial Department of EducationBengbu Medical CollegeChinese Academy of Sciences
KeywordsCommunity healthHealth promotionService-learningMedical educationNursingService (business)Promotion (chess)Occupational health nursingNurse educationHealth educationPsychologyMedicinePublic healthPedagogy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.106
GPT teacher head0.525
Teacher spread0.419 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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