Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/110157
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dc.contributorDepartment of Rehabilitation Sciences-
dc.creatorLeung, E-
dc.creatorLee, A-
dc.creatorLiu, Y-
dc.creatorHung, CT-
dc.creatorFan, N-
dc.creatorChing, SCC-
dc.creatorYee, H-
dc.creatorHe, Y-
dc.creatorXu, R-
dc.creatorTsang, HWH-
dc.creatorGuan, J-
dc.date.accessioned2024-11-28T02:59:49Z-
dc.date.available2024-11-28T02:59:49Z-
dc.identifier.urihttp://hdl.handle.net/10397/110157-
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.rightsCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Leung E, Lee A, Liu Y, Hung C-T, Fan N, Ching SCC, Yee H, He Y, Xu R, Tsang HWH, et al. Impact of Environment on Pain among the Working Poor: Making Use of Random Forest-Based Stratification Tool to Study the Socioecology of Pain Interference. International Journal of Environmental Research and Public Health. 2024; 21(2):179 is available at https://doi.org/10.3390/ijerph21020179.en_US
dc.subjectBuilt environmenten_US
dc.subjectMachine learningen_US
dc.subjectPain interferenceen_US
dc.subjectWorking pooren_US
dc.titleImpact of environment on pain among the working poor : making use of random forest-based stratification tool to study the socioecology of pain interferenceen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume21-
dc.identifier.issue2-
dc.identifier.doi10.3390/ijerph21020179-
dcterms.abstractPain interferes with one’s work and social life and, at a personal level, daily activities, mood, and sleep quality. However, little research has been conducted on pain interference and its socioecological determinants among the working poor. Noting the clinical/policy decision needs and the technical challenges of isolating the intricately interrelated socioecological factors’ unique contributions to pain interference and quantifying the relative contributions of each factor in an interpretable manner to inform clinical and policy decision-making, we deployed a novel random forest algorithm to model and quantify the unique contribution of a diverse ensemble of environmental, sociodemographic, and clinical factors to pain interference. Our analyses revealed that features representing the internal built environment of the working poor, such as the size of the living space, air quality, access to light, architectural design conducive to social connection, and age of the building, were assigned greater statistical importance than other more commonly examined predisposing factors for pain interference, such as age, occupation, the severity and locations of pain, BMI, serum blood sugar, and blood pressure. The findings were discussed in the context of their benefit in informing community pain screening to target residential areas whose built environment contributed most to pain interference and informing the design of intervention programs to minimize pain interference among those who suffered from chronic pain and showed specific characteristics. The findings support the call for good architecture to provide the spirit and value of buildings in city development.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational journal of environmental research and public health, Feb. 2024, v. 21, no. 2, 179-
dcterms.isPartOfInternational journal of environmental research and public health-
dcterms.issued2024-02-
dc.identifier.scopus2-s2.0-85186116483-
dc.identifier.pmid38397670-
dc.identifier.eissn1661-7827-
dc.identifier.artn179-
dc.description.validate202411 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextStrategic Public Policy Research Funding Schemeen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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