Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study.

ABSTRACT:

Background

Experiences of unfair treatment on college campuses are linked to adverse mental and physical health outcomes, highlighting the need for interventions. However, detecting such experiences relies mainly on self-reports. No prior research has examined the feasibility of using mobile sensing via smartphones and wearables for the passive detection of these experiences.

Objective

This pilot study explores the potential of using passive sensing to detect daily experiences of perceived unfair treatment (PUT) after they occur. It aims to develop and evaluate machine learning models against naive baselines and establish a benchmark for future research.

Methods

We analyzed data from 201 undergraduate students collected over two 10-week academic terms in 2018. PUT was

SUBMITTER: Ren Y 

PROVIDER: S-EPMC12619020 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature