Evidence map›Paper›PMID 33963597›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2021

A new robust Bayesian small area estimation via

Shaho Zarei, Serena Arima, Giovanna Jona Lasinio

Open access · hybridAbstract read
In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2021. 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
1.2field-weighted citation impact, top 20% of its field
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, 3 citations in OpenAlex.

  1. Article
  2. A new robust Bayesian small area estimation viaBiometrical journal. Biometrische Zeitschrift · 2021
    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

3 authors at 3 institutions in 2 countries.

Shaho ZareiDepartment of Statistics, Faculty of Science, University of Kurdistan, Kurdistan, Iran.
Serena ArimaDepartment of History, Social Science and Human Studies, University of Salento, Lecce, Italy.ORCID 0000-0001-6366-1592
Giovanna Jona LasinioDepartment of Statistical Sciences, University of Rome "La Sapienza", Roma, Italy.
Sapienza University of Rome · ITUniversity of Kurdistan · IRUniversity of Salento · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last few years, diabetes mellitus and obesity revealed to be one of the fastest-growing chronic diseases in youth in the United States. The number of new diabetes cases is dramatically increasing, and, for the moment, effective therapy does not exist. Experts believe that one of the causes of this increase is the decline in exercise behavior. The California Education Code requires local educational agencies (LEAs) to administer the FITNESSGRAM, the Physical Fitness Test (PFT), to Californian students of public schools. This test evaluates six fitness areas, and experts defined that a passing result on all six areas of the test represents a fitness level that offers some protection against the diseases associated with physical inactivity. We consider 2015-2016 data provided by the California Department of Education (CDE): for each Californian county (

Indexed as

ObesityPhysical FitnessAdolescentBayes TheoremHumansSchoolsStudentsUnited Statesarea-level modelCalifornia FITNESSGRAMhierarchical Bayesian modelsmall area estimationstable distribution

Identifiers

PMID33963597
PMCPMC8453931
OpenAlexW3158703515

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.