Ultra-rapid development and deployment of a family resilience program during the COVID-19 pandemic: Lessons learned from <i>Families Tackling Tough Times Together</i>
Bibliographic record
Abstract
The 2020 COVID-19 pandemic brought uncertainty, anxiety, and stress into households; however, it also created an opportunity as many families, sequestered at home, found themselves spending much more time together. To support families and improve their ability to cope, recover, and build resilience amid the pandemic, Purdue University’s College of Health and Human Sciences (HHS) launched Families Tackling Tough Times Together (FT), a strength-based multi-week online program informed by scientific evidence about family resilience. Offered through a Facebook group, FT targeted parents or caregivers, children, youth, young adults, older adults, and helping professionals serving families. FT was designed to appeal to both military and civilian families, in part because both groups were experiencing similar challenges associated with the pandemic. This was not only an opportunity to bring civilian and military families together, but also for civilian families to learn from the experiences of military families in surmounting significant challenges. This article describes the development and implementation of the FT program, as well as lessons learned. Strategies highlighted in this article may be helpful to researchers or practitioners who wish to implement a rapid-response intervention aimed at building family resilience.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".