Loneliness, Risk-Taking, and Attention Deficit Hyperactivity Disorder During the COVID-19 Pandemic
Bibliographic record
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder, commonly associated with impulsivity, risk-taking, and greater reported loneliness. Risk-taking can manifest in various ways, including reduced regard for health-conscious practices. Recent research indicates that individuals with ADHD may be less likely to follow pandemic safety instruction. (e.g., not following social distancing recommendations). Given that individuals with ADHD tend to discount the value of delayed reward in favour of immediate gratification, it can be presumed that individuals with greater ADHD symptomology may be more likely to not follow social distancing guidelines in an attempt to relieve acute feelings of loneliness. The present project is comprised of two studies that sought to better understand the relation between ADHD, loneliness, and risk-taking during the COVID-19 pandemic. Study 1 tested and validated a measure of COVID-related risk-taking (CrRT) that was developed for the main analysis in Phase I of Study 2. We investigated CrRT in 318 undergraduate students recruited from a Canadian university. With exploratory factor analysis, we identified a two-factor structure of COVID-related risk-taking: social non-avoidance and personal protection non-compliance, which is consistent with other related studies. Our scale was validated with results from multiple regression analysis showing that younger age, lower risk perception, lower stress, non-planning, and greater risk-tolerance significantly predicted public health risk-taking behaviours, also validated by previous work. Study 2 was divided into two phases. Phase I explored ADHD-related problems and health-risk behaviours in emerging adults within the context of the pandemic. Using multiple regression analysis, we investigated predictors of COVID-related risk-taking (i.e., reduced adherence to public health and safety measures) in 456 young adults. It was hypothesized that: (1) Participants with more ADHD-related symptoms would report greater loneliness, emotional dysregulation, and negative emotional states during the COVID-19 pandemic than non-ADHD individuals; and (2) Greater ADHD symptomology, loneliness, and substance use would predict risk-taking behaviours. Findings supported the first hypothesis and partially supported the second hypotheses. Participants with more ADHD symptoms reported higher ratings of psychological distress, emotional dysregulation, loneliness, and cannabis use compared to participants with low ADHD symptoms. More severe ADHD symptoms were predictive of COVID-related risk-taking, including non-adherence to pandemic health measures. Phase II used qualitative analysis to answer the following questions: (1) What are the overlapping themes in relation to resilience, emotion regulation, and successful coping?; and (2) What role does ADHD symptomology play in coping with pandemic-related difficulties? Results indicated that external-avoidance strategies (e.g., distraction) were the most frequently used, however, respondents rated internal-approach strategies (e.g., cognitive restructuring, acceptance, exercise) to be the most effective overall. Results suggest that psychological well-being is associated with strategies designed to engage with or move through distressing experiences, greater self-compassion, and emotional regulation. These studies provide an initial overview of coping and risk-taking behaviours during a global health crisis. Findings are consistent with similar research, which suggests that individuals with more severe ADHD symptoms are more vulnerable to distressing situations and, as a result, may engage in high-risk behaviours. The current dissertation’s findings highlight clear targets for intervention tailored for individuals with ADHD during times of heightened distress. Specifically, interventions may include skills-based training that facilitates emotion regulation strategies (e.g., distress tolerance, emotion recognition), self-compassion, problem solving, and attentive awareness. This research provides meaningful insights that may be used to guide intervention planning for individuals with ADHD symptomology in clinical and academic settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".