Enhancing resilience during the COVID-19 pandemic: A thematic analysis and evaluation of the warr;or21 program
Bibliographic record
Abstract
The novel coronavirus (COVID-19) has negatively impacted the world in a variety of ways. Thousands have died, many more have fallen ill, and it continues to have a disastrous impact on the global economy. The virus has also significantly impacted people’s well-being and their mental health, where the effects are expected to continue long after businesses begin to re-open. Promoting resilience and positive mental health coping strategies are, therefore, vital to assisting people as this pandemic continues and long after a sense of “normalcy” returns. This paper, a program analysis of warr;or21, a resilience program, utilizes qualitative research methods to share the insights of participants who completed the program during the COVID-19 pandemic. The warr;or21 program was designed initially to enhance resilience in law enforcement and other first responders and has since been adapted for the general public. The data reveals that, from the perspective of the participants, warr;or21 has helped many of them cope and manage positively, specifically amid the COVID-19 pandemic. Thus, the warr;or21 program has the potential to help enhance people’s resilience and mental health during future adverse events as well as to be used proactively to further develop a person’s overall mental health and 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 | 0.045 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".