Leveraging smartphones to observe couples remotely and illuminate how COVID-19 stress shaped marital communication.
Bibliographic record
Abstract
The height of the COVID-19 pandemic was an exceptionally stressful time for families that offered a unique opportunity to understand how stressful experiences occurring outside the relationship shape behavior occurring inside the relationship. Given the social distancing requirements of the pandemic, however, most research addressing this issue has relied on self-reports of behavior, which are susceptible to bias. In the summer of 2020, we asked a sample of married individuals living in the United States, Canada, Ireland, and the United Kingdom to complete online questionnaires assessing neuroticism and attachment insecurity, their levels of chronic stress, and their levels of acute stress due to the COVID-19 pandemic. We then asked participants to submit a 10-min video of themselves and their spouse attempting to solve an important marital problem that they recorded on their smartphone or other device and uploaded to a secure server. Coders were able to reliably code the behavior of both partners using an established coding system, and the distribution of codes was similar to prior research. Consistent with predictions, participants' COVID-19 stress interacted with their neuroticism and attachment avoidance to predict their levels of oppositional behavior, controlling for their levels of chronic stress and their partner's behavior; neuroticism and attachment avoidance were associated with behaving in a more oppositional manner among participants who reported high but not low COVID-19 stress. Attachment anxiety trended toward predicting more oppositional behavior regardless of stress. These results shed light on how stress affects behavior and introduce a novel way to observe family behavior remotely. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".