Writing narrative style literature reviews
Abstract
Reviews provide a synthesis of published literature on a topic and describe its current state-of-art. Reviews in clinical research are thus useful when designing studies or developing practice guidelines. The two standard types of reviews are (a) systematic and (b) non-systematic or narrative review. Unlike systematic reviews that benefit from guidelines such as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, there are no acknowledged guidelines for narrative reviews. I have attempted to define the best practice recommendations for the preparation of a narrative review in clinical research. The quality of a narrative review may be improved by borrowing from the systematic review methodologies that are aimed at reducing bias in the selection of articles for review and employing an effective bibliographic research strategy. The dynamics of narrative review writing, the organizational pattern of the text, the analysis, and the synthesis processes are also discussed.
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The review followed PRISMA guidelines and conducted a systematic search and narrative synthesis.
“Following preferred reporting for systematic reviews guidelines (PRISMA) (Page et al., 2020), we conducted a systematic search and narrative synthesis (Ferrari, 2015).”
From The Role and Impact of the Prison Chaplain: A Systematic Review and Narrative Synthesis (2024) · cites this paper as Ferrari, 2015
Due to methodological heterogeneity, all data were combined through a narrative synthesis.
“Due to methodological heterogeneity, we combined all data through a narrative synthesis, a dynamic process consisting data extraction and visualisation, description and interpretation (Ferrari, 2015).”
From The Role and Impact of the Prison Chaplain: A Systematic Review and Narrative Synthesis (2024) · cites this paper as Ferrari, 2015
The narrative review was conducted following international guidelines for writing narrative reviews.
“We conducted a narrative review of literature following international guidelines for writing narrative reviews [20, 21].”
From Cognitive decline following acute viral infections: literature review and projections for post-COVID-19 (2022) · cites this paper as [20]
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“An example of good title is: Injuries Associated with Soccer: A review of Epidemiology and Etiology.2 Literature search for the present article As an example, a literature search was performed for the present study on the lines of searches for an NR, but including features of SR methodology (Figure 1).”
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“However, the narrative literature review can be improved by borrowing from the systematic review methodologies (Ferrari, 2015), with the objective to synthesize results from multiple original studies by limiting systematic errors or bias (Cook et al.”
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- Authors
- Ferrari, R.
- Corresponding author
- Ferrari, Rossella
- Venue
- Medical Writing
- Publisher
- Informa UK Limited
- Published
- 2015-12
- Volume
- 24
- Issue
- 4
- Pages
- 230-235
- ISSN
- 2047-4806 (ISSN-L)
- 2047-4814
- OpenAlex
- W2239104709
- Cited by
- 1,907
- References
- 25
- Citations by year
- 2026: 462 · 2025: 327 · 2024: 273 · 2023: 212 · 2022: 214 · 2021: 175 · 2020: 122 · 2019: 65 · 2018: 43 · 2017: 10 · 2016: 1 · 2014: 1
- FWCI
- 16.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Writing (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Health Sciences › Health Professions › General Health Professions › Health Sciences Research and Education
- Keywords
- literature review writing, clinical research, best practice recommendations, systematic reviews, text structure
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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