Creative approaches to mixed-methods data collection in the context of COVID-19: Investigating families, emotions, and collective coping in a prospective sample
Bibliographic record
Abstract
This research brief describes an ongoing, multi-timepoint investigation of parental emotion socialization and child functioning. We utilized a prospective design to explore the impact of the COVID-19 pandemic on our research participants’ emotion functioning. This follow-up study included 102 parents who were initially interviewed and surveyed on psychological well-being, parenting behaviours, and child functioning. Researchers incorporated parent and child report measures alongside recorded parent-child discussions to comprehensively capture how families have coped during pandemic. This brief provides descriptions of secure methods for remotely collecting observational data that can be implemented using Qualtrics and Microsoft OneDrive. This method was generated by the researchers with both participant convenience and privacy in mind. This forthcoming study will further highlight the need to prospectively analyze the collective impact of COVID-19 within the family system. Methods described herein may inform future qualitative virtual research through increasing naturalism and accessibility to remote areas and diverse populations.
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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 | 0.278 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".