Prediction of Online Psychological Help-Seeking Behavior During the COVID-19 Pandemic: An Interpretable Machine Learning Method

Addressing health challenges in rural Japan: a thematic analysis of social isolation and community solutions

BACKGROUND

The establishment of sustainable connections between medical professionals and rural citizens is pivotal for effective community healthcare. Our study focuses on understanding and resolving health problems arising from social isolation, a critical barrier to achieving this goal, especially in the context of the coronavirus disease 2019(COVID-19) pandemic's impact on community dynamics respecting social cognitive theory. This study investigates the link between social isolation and rural community healthcare. We aim to develop methods that improve interaction and collaboration between healthcare providers and rural communities, ultimately enhancing the region's healthcare system.

METHODS

Employing thematic analysis based on social cognitive theory, we conducted semi-structured interviews with 57 community workers in rural communities. This qualitative approach enabled us to delve into the nuances of social isolation and its multifaceted impact on health and community well-being.

RESULTS

Our analysis revealed four key themes: the impact of aging on social dynamics, shifts in community relationships, unique aspects of rural community networking, and the role of these networks in driving community health. Notably, we identified specific challenges, such as the erosion of intergenerational interactions and the hesitancy to seek support, exacerbated by social isolation and negatively impacting community health.

CONCLUSIONS

Our study reveals the complex factors affecting rural community sustainability, particularly social isolation influenced by privacy concerns and changing social dynamics. Emphasizing the importance of social cognitive theory, it highlights the need for adaptable healthcare systems and strong community-medical collaborations. Future research should focus on developing culturally sensitive, practical strategies for enhancing these collaborations, especially involving physicians, to address rural communities' unique challenges.

Demand prediction of medical services in home and community-based services for older adults in China using machine learning

Background

Home and community-based services are considered an appropriate and crucial caring method for older adults in China. However, the research examining demand for medical services in HCBS through machine learning techniques and national representative data has not yet been carried out. This study aimed to address the absence of a complete and unified demand assessment system for home and community-based services.

Methods

This was a cross-sectional study conducted on 15,312 older adults based on the Chinese Longitudinal Healthy Longevity Survey 2018. Models predicting demand were constructed using five machine-learning methods: Logistic regression, Logistic regression with LASSO regularization, Support Vector Machine, Random Forest, and Extreme Gradient Boosting (XGboost), and based on Andersen's behavioral model of health services use. Methods utilized 60% of older adults to develop the model, 20% of the samples to examine the performance of models, and the remaining 20% of cases to evaluate the robustness of the models. To investigate demand for medical services in HCBS, individual characteristics such as predisposing, enabling, need, and behavior factors constituted four combinations to determine the best model.

Results

Random Forest and XGboost models produced the best results, in which both models were over 80% at specificity and produced robust results in the validation set. Andersen's behavioral model allowed for combining odds ratio and estimating the contribution of each variable of Random Forest and XGboost models. The three most critical features that affected older adults required medical services in HCBS were self-rated health, exercise, and education.

Conclusion

Andersen's behavioral model combined with machine learning techniques successfully constructed a model with reasonable predictors to predict older adults who may have a higher demand for medical services in HCBS. Furthermore, the model captured their critical characteristics. This method predicting demands could be valuable for the community and managers in arranging limited primary medical resources to promote healthy aging.

Effective Prediction and Important Counseling Experience for Perceived Helpfulness of Social Question and Answering-Based Online Counseling: An Explainable Machine Learning Model

The social question answering based online counseling (SQA-OC) is easy access for people seeking professional mental health information and service, has become the crucial pre-consultation and application stage toward online counseling. However, there is a lack of efforts to evaluate and explain the counselors' service quality in such an asynchronous online questioning and answering (QA) format efficiently. This study applied the notion of perceived helpfulness as a public's perception of counselors' service quality in SQA-OC, used computational linguistic and explainable machine learning (XML) methods suited for large-scale QA discourse analysis to build an predictive model, explored how various sources and types of linguistic cues [i.e., Linguistic Inquiry and Word Count (LIWC), topic consistency, linguistic style similarity, emotional similarity] contributed to the perceived helpfulness. Results show that linguistic cues from counselees, counselors, and synchrony between them are important predictors, the linguistic cues and XML can effectively predict and explain the perceived usefulness of SQA-OC, and support operational decision-making for counselors. Five helpful counseling experiences including linguistic styles of "talkative", "empathy", "thoughtful", "concise with distance", and "friendliness and confident" were identified in the SQA-OC. The paper proposed a method to evaluate the perceived helpfulness of SQA-OC service automatically, effectively, and explainable, shedding light on the understanding of the SQA-OC service outcome and the design of a better mechanism for SQA-OC systems.