Dataset Information

Social Media: Social Intelligence Training Module.

ABSTRACT:

Introduction

Physicians' posts on social media have the potential to impact the patient-physician relationship, interpersonal relationships at work, institutions' reputations, and the public's trust in health care professionals. Empirical research, along with several very public cases of unprofessional behavior by physicians on social media, suggests that resident physicians are not always aware of the implications of their actions online. Residency programs are being charged with ways to model positive online presence. The goal of this project was to develop a social media training program that emphasized perspective taking and fostered appropriate social media use.

Methods

This training program involves an interactive lecture and discussion, with approximately 20 minutes of content, 20 minutes of small-group discussion, and 10 minutes of large-group discussion. We evaluated the effectiveness of this program by asking participants to complete presession and postsession surveys of social media knowledge, attitudes, and behaviors.

Results

Survey responses (N = 16) suggest that the social media training program was successful. Participants demonstrated an increase in knowledge of social media policies, ability to identify potentially inappropriate media interactions, ability to identify appropriate responses to such interactions, and understanding of how their actions on social media affect others.

Discussion

We believe that the social media module is an effective and useful tool for members of the medical community as the internet and social media continue to grow in popularity and lines between professional and personal realms are continually blurred. While the effectiveness of this program was established with first-year pediatric residents, the module material is applicable to a broader medical audience.

SUBMITTER: Robertson M

PROVIDER: S-EPMC6464464 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Social Media: Social Intelligence Training Module.

Robertson Morgan M Shoss Mindy K MK Broom Matthew A MA

MedEdPORTAL : the journal of teaching and learning resources 20160826

<h4>Introduction</h4>Physicians' posts on social media have the potential to impact the patient-physician relationship, interpersonal relationships at work, institutions' reputations, and the public's trust in health care professionals. Empirical research, along with several very public cases of unprofessional behavior by physicians on social media, suggests that resident physicians are not always aware of the implications of their actions online. Residency programs are being charged with ways t ...[more]

PMID: 31008220

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Project description:BackgroundSocial media sites are becoming an increasingly important source of information about mental health disorders. Among them, eating disorders are complex psychological problems that involve unhealthy eating habits. In particular, there is evidence showing that signs and symptoms of anorexia nervosa can be traced in social media platforms. Knowing that input data biases tend to be amplified by artificial intelligence algorithms and, in particular, machine learning, these methods should be revised to mitigate biased discrimination in such important domains.ObjectiveThe main goal of this study was to detect and analyze the performance disparities across genders in algorithms trained for the detection of anorexia nervosa on social media posts. We used a collection of automated predictors trained on a data set in Spanish containing cases of 177 users that showed signs of anorexia (471,262 tweets) and 326 control cases (910,967 tweets).MethodsWe first inspected the predictive performance differences between the algorithms for male and female users. Once biases were detected, we applied a feature-level bias characterization to evaluate the source of such biases and performed a comparative analysis of such features and those that are relevant for clinicians. Finally, we showcased different bias mitigation strategies to develop fairer automated classifiers, particularly for risk assessment in sensitive domains.ResultsOur results revealed concerning predictive performance differences, with substantially higher false negative rates (FNRs) for female samples (FNR=0.082) compared with male samples (FNR=0.005). The findings show that biological processes and suicide risk factors were relevant for classifying positive male cases, whereas age, emotions, and personal concerns were more relevant for female cases. We also proposed techniques for bias mitigation, and we could see that, even though disparities can be mitigated, they cannot be eliminated.ConclusionsWe concluded that more attention should be paid to the assessment of biases in automated methods dedicated to the detection of mental health issues. This is particularly relevant before the deployment of systems that are thought to assist clinicians, especially considering that the outputs of such systems can have an impact on the diagnosis of people at risk.

Dataset Information

Social Media: Social Intelligence Training Module.

Introduction

Methods

Results

Discussion

Publications

Social Media: Social Intelligence Training Module.

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