Evidence map›Paper›PMID 23971944›Full record

ArticleBMC medical informatics and decision making2013

Analyzing the impact of social factors on homelessness: a fuzzy cognitive map approach.

Vijay K Mago, Hilary K Morden, Charles Fritz, Tiankuang Wu, Sara Namazi, Parastoo Geranmayeh, Rakhi Chattopadhyay, Vahid Dabbaghian

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. The Role of Street Medicine and Mobile Clinics for Persons Experiencing Homelessness: A Scoping Review.International journal of environmental research and public health · 2024
    Article
  7. Article
  8. Article
  9. Article
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  13. The Relational Vulnerability of People Experiencing Multiple Exclusion Homelessness (MEH) in Spain.International journal of environmental research and public health · 2021
    Article
  14. Article
  15. Article
  16. Addressing the Intersection of Homelessness and Mental Illness: A Critical Analysis of Treatment and Rehabilitation Frameworks in India.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine
    Article
  17. 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

8 authors.

Vijay K MagoThe Modelling of Complex Social Systems (MoCSSy) Program, The IRMACS Centre, Simon Fraser University, Burnaby, Canada. vmago@sfu.ca.
Hilary K Morden
Charles Fritz
Tiankuang Wu
Sara Namazi
Parastoo Geranmayeh
Rakhi Chattopadhyay
Vahid Dabbaghian

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe forces which affect homelessness are complex and often interactive in nature. Social forces such as addictions, family breakdown, and mental illness are compounded by structural forces such as lack of available low-cost housing, poor economic conditions, and insufficient mental health services. Together these factors impact levels of homelessness through their dynamic relations. Historic models, which are static in nature, have only been marginally successful in capturing these relationships.

methodsFuzzy Logic (FL) and fuzzy cognitive maps (FCMs) are particularly suited to the modeling of complex social problems, such as homelessness, due to their inherent ability to model intricate, interactive systems often described in vague conceptual terms and then organize them into a specific, concrete form (i.e., the FCM) which can be readily understood by social scientists and others. Using FL we converted information, taken from recently published, peer reviewed articles, for a select group of factors related to homelessness and then calculated the strength of influence (weights) for pairs of factors. We then used these weighted relationships in a FCM to test the effects of increasing or decreasing individual or groups of factors. Results of these trials were explainable according to current empirical knowledge related to homelessness.

resultsPrior graphic maps of homelessness have been of limited use due to the dynamic nature of the concepts related to homelessness. The FCM technique captures greater degrees of dynamism and complexity than static models, allowing relevant concepts to be manipulated and interacted. This, in turn, allows for a much more realistic picture of homelessness. Through network analysis of the FCM we determined that Education exerts the greatest force in the model and hence impacts the dynamism and complexity of a social problem such as homelessness.

conclusionsThe FCM built to model the complex social system of homelessness reasonably represented reality for the sample scenarios created. This confirmed that the model worked and that a search of peer reviewed, academic literature is a reasonable foundation upon which to build the model. Further, it was determined that the direction and strengths of relationships between concepts included in this map are a reasonable approximation of their action in reality. However, dynamic models are not without their limitations and must be acknowledged as inherently exploratory.

Indexed as

Fuzzy LogicIll-Housed PersonsBritish ColumbiaGeographic MappingHumansModels, PsychologicalModels, StatisticalSocioeconomic Factors

Identifiers

PMID23971944
PMCPMC3766254

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.