Evidence map›Paper›PMID 33757585›Full record

ArticleBMC sports science, medicine & rehabilitation2021

Random forest model to identify factors associated with anabolic-androgenic steroid use.

Zohreh Manoochehri, Majid Barati, Javad Faradmal, Sara Manoochehri

Abstract read
In one paragraph

Article in BMC sports science, medicine & rehabilitation, 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
–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
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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.

Zohreh ManoochehriDepartment of Biostatistics, Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran.
Majid BaratiDepartment of Public Health, School of Health, Autism Spectrum Disorders Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Javad FaradmalModeling of Noncommunicable Diseases Research Center, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Shahid Fahmideh Boulevard, Hamadan, Iran. javad.faradmal@umsha.ac.ir.ORCID https://orcid.org/0000-0001-5514-3584
Sara ManoochehriDepartment of Biostatistics, Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOne of the types of doping that is commonly used by bodybuilders, is androgenic-anabolic steroids (AAS). The use of AAS besides violating sporting ethics would have serious consequences on physical and mental health statuses. This study aimed to determine the most important factors of using AAS among bodybuilders by prototype willingness model (PWM).

methodsIn this analytical cross-sectional study, 280 male bodybuilders were selected from the bodybuilding clubs in Hamadan city using multistage sampling in 2016. A self-administered questionnaire consisting of demographic information and constructs of the PWM was then used to collect data and random forest model was also applied to analyze the collected data.

resultsBehavioral willingness, attitude, and previous AAS use were found as the most important factors in determining the behavioral intention. Moreover, subjective norms, attitude, BMI, and prototypes were the factors with the greatest effect on predicting behavioral willingness of AAS use. As well, behavioral intention was observed to be more important than behavioral willingness for predicting of AAS use. DISCUSSION: The obtained results show that the reasoned action path has a greater impact to predict AAS use among bodybuilders compared to social reaction path.

Indexed as

Androgenic-anabolic steroidsPrototype willingness modelRandom forest

Identifiers

PMID33757585
PMCPMC7988984

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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.