ReviewFrontiers in public health2026
Digital social prescribing: a concept analysis.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Digital Social Prescribing (DSP) represents an innovative paradigm that integrates digital technologies into traditional social prescribing frameworks to address the social determinants of health (SDOH). However, persistent conceptual inconsistencies and a paucity of theoretical clarity have constrained its systematic application and evaluation within public health and nursing practices. Aims: This study aims to clarify the definition of digital social prescribing, distinguish it from traditional social prescribing, and identify its core characteristics through a systematic concept analysis. Methods: The Walker and Avant concept analysis framework was adopted. A comprehensive literature searches was conducted across multiple databases, including PubMed, CINAHL (EBSCOhost), APA PsycArticles, Scopus, Web of Science, Embase, IEEE Xplore, ACM Digital Library. A total of 30 relevant articles were included in the analysis. The analysis followed eight steps proposed by Walker and Avant: selecting concept, determining purposes of the analysis, identifying all uses of concept, determining defining attributes, constructing model and related cases, identifying antecedents and consequences, and defining empirical referents. Results: Digital social prescribing (DSP) is not merely a technological tool but a multidimensional public health model. Its five defining attributes are the uses of technology, non-clinical services, make personal plans based on needs, likes and location, community-based resources, and organizations from different sectors. The antecedents of DSP include contextual and population health drivers, medical systems and structural limitations, technological enabling factors, practice and implementation gaps and catalytic events and social development trends. The consequences of DSP encompass health and well-being outcomes, care and service delivery, system and implementation outcomes, equity and ethical considerations. Despite its potential benefits, DSP also faces challenges, including digital exclusion, data governance issues, gender imbalances and structural ethnic disparities. Conclusion: This study provides a comprehensive conceptual framework for digital social prescribing (DSP), addressing existing conceptual ambiguities and clarifying its theoretical boundaries. The study also highlights the need to critically address potential challenges associated with DSP implementation, including digital exclusion, data governance concerns, gender imbalances and structural ethnic disparities. These findings support the development of standardized evaluation frameworks and provide guidance for future research, practice and policy aimed at facilitating the sustainable and equitable implementation of DSP.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.