Evidence map›Paper›PMID 39650184›Full record

ArticleHeliyon2024

Enhancing systematic literature review adapting 'double diamond approach'.

Hari Prasad Pandey, Tek Narayan Maraseni, Armando A Apan

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hari Prasad PandeyDepartment of Forests and Soil Conservation, Ministry of Forests and Environment, Kathmandu, Nepal.
Tek Narayan MaraseniCentre for Sustainable Agricultural Systems (CSAS), University of Southern Queensland, Toowoomba, Queensland, 4350, Australia.
Armando A ApanCentre for Sustainable Agricultural Systems (CSAS), University of Southern Queensland, Toowoomba, Queensland, 4350, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current literature review frameworks often lack quantification of literature volume, guidance on keyword inclusion, confidence in identifying knowledge gaps, and the formulation of research questions, which can lead to potential bias in research. This undermines the validity and reliability of reviews. In this paper, we aim to enhance the systematic literature review (SLR) methods-starting with a review of review literature followed by a review of empirical literature-to address these challenges. By critically evaluating existing SLR approaches, we propose an enhanced SLR method, termed 'double-stage SLR,' designed to identify gaps, define research questions, develop strategies to overcome challenges, and deliver high-quality review work. This approach mirrors the Double Diamond Approach (DDA), which we refer to as 'DDA in SLR', viewing systematic reviews as projects aimed at addressing existing issues in review processes. The DDA in SLR involves a two-stage process: first, reviewing existing reviews to formulate research questions, and then examining empirical research to meet review objectives. This method does not challenge existing SLR frameworks but rather acknowledges them as the foundation for enhancement by systematically reviewing both review and empirical literature, ultimately optimizing review quality. Additionally, this enhanced method reduces risks and bias in SLR, as validated through practical application, and encourages its use to produce high-quality outputs in research, academia, and beyond.

Indexed as

Literature searchingResearch biasSystematic reviewTwo-stage process

Identifiers

PMID39650184
PMCPMC11625145

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.