Evidence map›Paper›PMID 31134318›Full record

ArticleInternational journal of public health2019

A fuzzy approach for analysing equitable and sustainable well-being in Italian regions.

Annamaria Porreca, Salvador Cruz Rambaud, Francesca Scozzari, Marta Di Nicola

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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.

Annamaria PorrecaDepartment of Economics, G. d'Annunzio University of Chieti-Pescara, Pescara, Italy.
Salvador Cruz RambaudDepartment of Economics and Business, Universidad de Almería, La Cañada de San Urbano, s/n, 04120, Almería, Spain. scruz@ual.es.
Francesca ScozzariDepartment of Economics, G. d'Annunzio University of Chieti-Pescara, Pescara, Italy.
Marta Di NicolaDepartment of Medical Oral Science and Biotechnology, G. d'Annunzio University of Chieti-Pescara Chieti, Chieti, Italy.

Funding

Ministerio de Economía, Industria y Competitividad, Gobierno de España DER2016-76053R
6 · The paper itself

Abstract

objectivesRecently, the Italian Institute of Statistics (ISTAT) and the National Council for Economy and Labor (CNEL) have proposed a measure for the equitable and sustainable well-being called the BES ("Benessere Equo e Sostenibile"). This paper aims to propose an original application of the fuzzy k-means approach to providing an analysis of the Italian regions according to their BES.

methodsThe fuzzy k-means algorithm was used for clustering the Italian regions according to BES data 2015. Afterwards, a principal component analysis was conducted to show and interpret the results.

resultsThere is a clear difference between the regions of the North and the South. The only exceptions are represented by Lazio and Abruzzo, which belong to both groups with almost equal degrees of truth. Moreover, Trentino-Alto Adige and Valle d'Aosta exhibit the best condition, whilst Molise is the worst region.

conclusionsThis study reveals that some Italian regions are in a state of backwardness regarding health, environment, minimum economic conditions, subjective well-being, education, employment conditions, social relationships, and working conditions. Therefore, institutions should consider local policies to address these issues.

Indexed as

Educational StatusHealth StatusAdultAgedAged, 80 and overAlgorithmsCluster AnalysisEconomic StatusEmploymentFemaleGeographyHumansItalyMaleMiddle AgedSocioeconomic FactorsBESFuzzy k-meansInequalitiesItalian regionsWell-being

Identifiers

What OpenQuestion holds

Textmetadata
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.