Evidence map›Paper›PMID 36091930›Full record

ArticleApplied intelligence (Dordrecht, Netherlands)2022

Large-scale group decision-making (LSGDM) for performance measurement of healthcare construction projects: Ordinal Priority Approach.

Amin Mahmoudi, Mehdi Abbasi, Jingfeng Yuan, Lingzhi Li

Abstract read
In one paragraph

Article in Applied intelligence (Dordrecht, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Amin MahmoudiDepartment of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, China.ORCID 0000-0001-7091-5044
Mehdi AbbasiDepartment of Industrial Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
Jingfeng YuanDepartment of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, China.
Lingzhi LiSchool of Civil Engineering, Nanjing Tech University, Nanjing, 211800 Jiangsu China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

People with various skill sets and backgrounds are usually found working on projects and thus, group decision-making (GDM) is one of the most important functions within any project. However, when projects concern healthcare or other critical services for proletariat or general public (especially during COVID19), the importance of GDM can hardly be overstated. Measuring the performance of healthcare construction projects is a critical activity and should be gauged based on the input from a large number of stakeholders. Such problems are usually recognized as large-scale group decision-making (LSGDM). In the current study, we aim to propose a decision support system for measuring the performance of healthcare construction projects against a large number of experts using ordinal data. The study identifies several key indicators from literature and recorded the observations of a large number of experts about these indicators. After that, the acceptable range of complexity is specified, the Silhouette plot is provided to find the optimal number of clusters, and the ordinal K-means method is employed to cluster the experts' opinions. Later, the confidence level is measured using a novel

Indexed as

Healthcare construction projectsLarge-scale group decision-makingMultiple criteria decision analysisOrdinal K-meansOrdinal priority approachWeighted Kendall’s W

Identifiers

PMID36091930
PMCPMC9449288

What OpenQuestion holds

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Registered trials

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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.