A Sociotechnical Model for Managing Mental Health Distress Among College Students During and After a Pandemic: Development and Usability Study
Bibliographic record
Abstract
Background Mental distress affects people's health in many ways and at different levels. However, anxiety and depressive disorder have significantly increased since the outbreak of the COVID-19 pandemic. Controlling the spread of COVID-19 resulted in isolation protocols such as stay-at-home orders, social distancing, and quarantining. Though well intended, the protocols exacerbated the already increasing number of mental distress cases prior to the pandemic. During the rollout of these pandemic interventions to control the spread, there was a noticeable increase in technology use. For instance, to cope with their mental health concerns, several people, including students, turned to technology to sustain their connection to the society and to access mental health services. Although a plethora of technological tools exist for communication and socialization, it is unknown which types of technologies are effective in the management of anxiety and depression symptoms. Hence, there is a need for a sociotechnical model that can identify technologies effective in addressing an individual's mental health symptoms, specifically among college students. Objective The objective of this study is to identify the effectiveness of current technologies used in coping with a mental distress situation and to develop a model that college students can use to effectively handle their mental health distress during and after a pandemic. Methods The proposed model is built on the Stallman's Health Theory of Coping. The model expands the theory with 5 significant components, namely Mental Health Distress Situation, Level of Distress, Coping Strategy, Technology Used, and the Mental Health Distress Outcome. This paper describes the conceptualized functionality of each component. The model will be implemented as a prototype mobile app and evaluated using a case study with students from 2 colleges. Results The study is underway. However, the model will be evaluated using 2 categories of nonrandomized focus groups of college students to determine the usefulness and the effectiveness of the model. Each group will consist of 8 participants. Data collected from each group will be qualitatively evaluated to identify themes from the responses, which will be used to refine the model to meet the study objective. Conclusions Many people experienced an increase in mental distress due to the isolation requirements arising from the COVID-19 pandemic. With limited access to traditional coping strategies in public and large gatherings, people turned to technology to manage their stress, anxiety, and depression. However, there is no “one-size-fits-all” technology that can address every individual distress level and coping strategy. Thus, developing a model to identify effective technologies used as coping strategies will be helpful for an individual in alleviating their mental health distress symptoms during and after a pandemic. Conflicts of Interest None declared.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".