‘A world of competing sorrows’: A mixed methods analysis of media reports of children with cancer abandoning conventional treatment

Diorio, Caroline; Afanasiev, Michael; Salena, Kristen; Marjerrison, Stacey

doi:10.1371/journal.pone.0209738

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…In terms of disease prevention, it is important to understand potential obstacles to population-based intervention approaches such as vaccination [25,32,33,110]. Patients' opinions can help health practitioners gain insight into the reasons why some patients may opt for traditional and complementary medicine [109]. Alternatively, understanding patients' experience with different treatments can support creation of personalized therapy plans [45].…”

Section: Areas and Applicationsmentioning

confidence: 99%

“…In particular, such information can help carers provide better care to patients [70]. [44,45,75,109], oral [110], lung [71], breast [53,54,67,68,70,71,74,76], cervical [110], prostate [21], colorectal [30,74,76], and cancer screening [38] Cancer [34], depression [47,48,111], suicide [51,[82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100], and dementia [40] Mental health diabetes [41,43,44,60,…”

Section: Areas and Applicationsmentioning

confidence: 99%

“…In addition to the choice of methods, their performance largely depends on the choice of features used to represent text. To support basic linguistic preprocessing, most studies used Stanford CoreNLP [136] (eg, [23,61,63,88,89,95,96,98,99,113]) and Natural Language Toolkit [137] (eg, [51,67,91,96,107,109]). Both libraries represent general purpose NLP tools, which may not be suitable for processing certain sublanguages [138].…”

Section: Scikit-learn [120] [45]mentioning

confidence: 99%

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Sentiment Analysis in Health and Well-Being: Systematic Review

Žunić¹,

Corcoran²,

Spasić³

2020

JMIR Med Inform

144

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Background Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” Objective This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals. Methods Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation. Results The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. Conclusions SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

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Section: Areas and Applicationsmentioning

confidence: 99%

Section: Areas and Applicationsmentioning

confidence: 99%

Section: Scikit-learn [120] [45]mentioning

confidence: 99%

See 1 more Smart Citation

Sentiment Analysis in Health and Well-Being: Systematic Review

Žunić¹,

Corcoran²,

Spasić³

2020

JMIR Med Inform

144

View full text Add to dashboard Cite

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State of Mental Health and Social Media: Analysis, Challenges, Advancements

Patil

Jain

Mohapatra

et al. 2022

Cognitive Intelligence and Big Data in Healthcare

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Nutritional traditional and complementary medicine strategies in pediatric cancer: A narrative review

Diorio

Kelly

Afungchwi

et al. 2020

Pediatric Blood & Cancer

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Traditional and complementary medicine (T&CM) strategies are commonly used by pediatric cancer patients. Nutritional approaches to T&CM include bioactive compounds, supplements, and herbs as well as dietary approaches. Pediatric cancer patients and their families commonly request and use nutritional T&CM strategies. We review the potential risks and benefits of nutritional T&CM use in pediatric cancer care and provide an overview of some commonly used and requested supplements, including probiotics, antioxidants, cannabinoids, vitamins, turmeric, mistletoe, Carica papaya, and others. We also discuss the role of specific diets such as the ketogenic diet, caloric restriction diets, whole‐food diets, and immune modulating diets. There is a growing body of evidence to support the use of some T&CM agents for the supportive care of children with cancer. However, further study is needed into these agents and approaches. Open communication with families about T&CM use is critical.

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‘A world of competing sorrows’: A mixed methods analysis of media reports of children with cancer abandoning conventional treatment

Cited by 4 publications

References 15 publications

Sentiment Analysis in Health and Well-Being: Systematic Review

Sentiment Analysis in Health and Well-Being: Systematic Review

State of Mental Health and Social Media: Analysis, Challenges, Advancements

Nutritional traditional and complementary medicine strategies in pediatric cancer: A narrative review

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