Evidence map›Paper›PMID 37131432›Full record

ArticleHeliyon2023

FLAVIA-LCT - Framework for systematic literature review to analyse vast InformAtion in life cycle thinking studies.

Teresa Maria Gulotta, Roberta Salomone, Giovanni Mondello, Bruno Ricca

Abstract read
In one paragraph

Article in Heliyon, 2023. 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

4 authors.

Teresa Maria GulottaUniversity of Messina, Department of Economics, Piazza Pugliatti 1, Messina, 98122, Italy.
Roberta SalomoneUniversity of Messina, Department of Economics, Piazza Pugliatti 1, Messina, 98122, Italy.
Giovanni MondelloUniversity of Messina, Department of Economics, Piazza Pugliatti 1, Messina, 98122, Italy.
Bruno RiccaUniversity of Messina, Department of Economics, Piazza Pugliatti 1, Messina, 98122, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the increasing interest in Life Cycle Thinking (LCT) applications for assessing the sustainability of processes, products and services, up-to-date syntheses and evidence-based critical outcomes analysis are required to guide future studies and policymakers. The systematic literature review is probably the most suitable approach for highlighting evidence of effects, impacts, and methodological choices, mapping the current knowledge and gaps in LCT fields, including methods such as Life Cycle Assessment, Life Cycle Costing, Social Life Cycle Assessment and Life Cycle Sustainability Assessment. Although several statements and guidelines for health care and ecology disciplines and one checklist for systematic literature review limited to Life Cycle Assessment (STARR-LCA) exist, it is still missing a framework for conducting systematic literature reviews in LCT field. This paper proposes a "Framework for systematic Literature review to Analyse Vast InformAtion in Life Cycle Thinking studies" (FLAVIA-LCT) to assist and guide researchers in structuring the processes of gathering, synthesising, and reporting outcomes from search strategy development to critical evaluation, considering the essential information to be included in a review manuscript. This framework can be used by anyone planning a literature review on one or more LCT methods.

Indexed as

Life Cycle Assessment (LCA)Life Cycle Costing (LCC)Life Cycle Sustainability Assessment (LCSA)Social Life Cycle Assessment (S-LCA)systematic review

Identifiers

PMID37131432
PMCPMC10149198

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.