Mathematical Model by Using Logistic Regression to Investigate the COVID-19 Pandemic's Impact on Humans
Bibliographic record
Abstract
The COVID-19 pandemic has precipitated profound psychosocial disturbances and shifts in consumer behavior due to stress, uncertainty, and imposed social distancing measures.Consequently, an investigation was conducted to elucidate the pandemic's impact and inform potential mitigation strategies.A comprehensive online survey was undertaken, involving 239 participants, focusing primarily on vulnerable groups prone to developing Post-Traumatic Stress Disorder (PTSD), anxiety, and depression, such as children, college students, and healthcare workers.In addition, a logistic regression and multiple regression analyses were employed to examine the pandemic's influence on consumer spending behaviors across 12 sectors.Changes in spending constraints were evaluated using a t-test.Results indicated an increased likelihood of PTSD, anxiety, and depression among children, college students, and healthcare workers due to pandemic exposure.Furthermore, five dominant factors were found to significantly influence consumer behavior: availability of essentials, financial security, health concerns, public sentiment, and quality of purchasable goods.This study proposes that, during a pandemic, businesses should adapt their strategies in accordance with changing consumer behaviors to gain valuable market insight, boost sales, and accelerate the introduction of new products to the market.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".