COVID-19: Modeling Out-of-Home and In-Home Activity Participation during the Pandemic
Bibliographic record
Abstract
Understanding the interaction between in-home and out-of-home activity participation decisions is important, particularly at a time when opportunities for out-of-home activities such as shopping, entertainment, and so forth are limited because of the COVID-19 pandemic. The travel restrictions imposed as a result of the pandemic have had a massive impact on out-of-home activities and have changed in-home activities as well. This study investigates in-home and out-of-home activity participation during the COVID-19 pandemic. Data comes from the COVID-19 Survey for assessing Travel impact (COST), conducted from March to May in 2020. This study uses data for the Okanagan region of British Columbia, Canada to develop the following two models: a random parameter multinomial logit (RPMNL) model for out-of-home activity participation and a hazard-based random parameter duration (HRPD) model for in-home activity participation. The model results suggest that significant interactions exist between out-of-home and in-home activities. For example, a higher frequency of out-of-home work-related travel is more likely to result in a shorter duration of in-home work activities. Similarly, a longer duration of in-home leisure activities might yield a lower likelihood for recreational travel. Health care workers are more likely to engage in work-related travel and less likely to participate in personal and household maintenance activities at home. The model confirms heterogeneity among the individuals. For instance, a shorter duration of in-home online shopping yields a higher probability for participation in out-of-home shopping activity. This variable shows significant heterogeneity with a large standard deviation, which reveals that sizable variation exists for this variable.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".