Taking action on the social determinants of health in clinical practice: a framework for health professionals

Andermann, Anne

doi:10.1503/cmaj.160177

Cited by 420 publications

(414 citation statements)

References 52 publications

(44 reference statements)

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“…Therefore, the main barriers to health access for the urban poor related to interacting effects of poverty 18 . Migrants did not use the health service in spite of the workers having a health insurance card and the distance from home to health center was not too far.…”

Section: Discussionmentioning

confidence: 99%

Accessibility to health services among migrant workers in the Northeast of Thailand

Khongthanachayopit

Laohasiriwong

2017

F1000Res

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Background. There is an increasing trend of trans-border migration from neighboring countries to Thailand. According to human rights laws, everyone must have access to health services, even if they are from other nationalities. However, a small minority of health personnel in Thailand discriminate against immigrant workers, as they are from a lower financial bracket. Methods. This cross-sectional study aims to determine the prevalence of accessibility to health services and factors associated with access to health services among migrant workers who work along the Northeast border of Thailand. A total of 621 legal migrant workers were randomly selected to respond to a structured questionnaire about the satisfaction of health services, using the 5As of health services: availability; accessibility; accommodation; affordability; acceptability. Associations between independent variables and access to health services were analysed using multiple logistic regression analysis. Results. The results indicated that the majority of these registered migrant workers were female (63.9%) with an average age of 29± 8.61 years old, and were married (54.3%). Most of the workers worked at restaurants (80%), whereas only 20% were in agricultural sectors. Only 14% (95% CI: 11-17%) of migrant workers had access to health services. The factors that were significantly associated with accessibility to health service experienced ill health during the past one year (OR = 2.48; 95%CI; 1.54–3.97; p-value<0.001) ; have been married (OR = 2.32; 95% CI: 1.40 – 3.90; p-value <0.001). Conclusions. Most of the migrant workers could not access health services. The ones who did access health services were married or ill.

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Section: Discussionmentioning

confidence: 99%

Accessibility to health services among migrant workers in the Northeast of Thailand

Khongthanachayopit

Laohasiriwong

2017

F1000Res

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“…The health care community and the patients’ own communities need to join forces to address the SDOH of the residents in their area. For example, when urban planners include strategies such as safe paths for walking and biking, parks that can be used for play and community gardens, and mobile grocers, residents have increased access to healthy foods …”

Section: Strategies For Successmentioning

confidence: 99%

“…Because it is not always obvious that a patient is being affected by SDOH, perioperative nurses can work to foster open communication and identify a patient's SDOH by asking tactful questions, providing information about local resources, and advising patients on actions they can take. Social determinants of health may be directly related to the patient's health condition . An example of this is a patient with a sedentary lifestyle because he or she does not have access to a safe place to exercise.…”

Section: Strategies For Successmentioning

confidence: 99%

Back to Basics: Social Determinants of Health

Spruce¹

2019

AORN Journal

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Social determinants of health (SDOH) are the conditions that people are born into and live under that affect their health. Nurses are responsible for addressing these determinants when possible. Perioperative nurses encounter the effects of SDOH when patients express concern about surgery scheduling times, transportation, child care, and other support‐related issues during the perioperative period. Some SDOH are related to social identity (eg, race, ethnicity, sexual orientation) as a result of the oppression and discrimination that some groups experience. Perioperative nurses can establish trust with their patients by identifying their needs and partnering with community‐based organizations to address some of those needs (eg, transportation, legal assistance), which may help relieve stress for their patients and promote better postsurgical outcomes. Although health care workers can assist individual patients to address the effects of SDOH on their health and life, policy change is needed to ensure long‐term benefits.

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“…For example, the zip code of a person can affect the healthcare conditions more than genomic factors. Therefore, SDOH should be actively included in clinical practice [37] and AI development.…”

Section: Social Determinants Of Healthmentioning

confidence: 99%

Status and Direction of Healthcare Data in Korea for Artificial Intelligence

Park

Shin

2017

Hanyang Med Rev

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Predicting temporal patterns across various domains poses significant challenges due to their nuanced and often nonlinear trajectories. To address this challenge, prediction frameworks have been continuously refined, employing data-driven statistical methods, mathematical models, and machine learning. Recently, as one of the challenging systems, shared transport systems such as public bicycles have gained prominence due to urban constraints and environmental concerns. Predicting rental and return patterns at bicycle stations remains a formidable task due to the system's openness and imbalanced usage patterns across stations. In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. The cartogram approach facilitates the prediction of demand for newly installed stations with no training data as well as long-period prediction, which has not been achieved before. We apply this method to public bicycle rentaland-return data in Seoul, South Korea, employing a spatial-temporal convolutional graph attention network. Our improved architecture incorporates batch attention and modified node feature updates for better prediction accuracy across different time scales. We demonstrate the effectiveness of our framework in predicting temporal patterns and its potential applications.

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Taking action on the social determinants of health in clinical practice: a framework for health professionals

Cited by 420 publications

References 52 publications

Accessibility to health services among migrant workers in the Northeast of Thailand

Accessibility to health services among migrant workers in the Northeast of Thailand

Back to Basics: Social Determinants of Health

Status and Direction of Healthcare Data in Korea for Artificial Intelligence

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