The impact of the COVID-19 pandemic on the self-management of Type 2 Diabetes: An Investigation of Reddit Data (Preprint)
Bibliographic record
Abstract
BACKGROUND Type 2 Diabetes (T2D) is a chronic disease that can be managed in part through healthy behaviours. However, the COVID-19 pandemic impacted how people managed their condition. Using social media forums and analytics through Patient-Generated Health Data (PGHD) presents an opportunity to understand the health behaviours from the perspective of the patient. OBJECTIVE Our objective is to understand how the health behaviours and attitudes of people living with T2D were impacted by the early stages of the COVID-19 pandemic by examining Reddit forums (using PGHD) for people living with T2D. METHODS Data from the Reddit forums related to T2D from January 2018 to early March 2021 were downloaded, and Support Vector Machines (SVMs) were used to classify if a post was made in the context of the pandemic. Latent Dirichlet Allocation (LDA) topic modelling was performed to gather topics of discussion amongst the entire dataset and a subsequent iteration was performed to gather topics of discussion specific to the COVID-19 pandemic. Sentiment Analysis using the Valence Aware Dictionary for sEntiment Reasoning (VADER )algorithm was performed to gauge attitudes towards the pandemic. RESULTS Of all posts, topics of discussion were classified into themes of Managing Lifestyle, Managing Blood Glucose, Obtaining Diabetes Care, and Coping & Receiving Support. Amongst the COVID-specific posts topics of discussion were Coping with Poor Mental Health, Accessing Doctor & Medications and Controlling Blood Glucose, Changing Food Habits during Pandemic, Impact of Stress of Blood Glucose Levels, Changing Status of Employment & Insurance, Risk of COVID Complications. Overall, posts classified as COVID-related had were associated with lower sentiment than those classified as “noncovid.” CONCLUSIONS Topics of discussion gauged from the Reddit forums provide a holistic perspective of the impact of the pandemic on people living with T2D. Overall, the early stages of the pandemic negatively impacted the attitudes of people living with T2D.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".