A commentary on Brazilian perspectives of lessons learned during the COVID-19 pandemic
Bibliographic record
Abstract
Background: While the pandemic brought many challenges and disruption to an individual’s life, it also presented individuals with the opportunity to develop coping strategies and seek changes in their lives. Brazilians experiencing that moment disclosed the uniqueness of learned lessons. Methods: A commentary written by the interviewers and transcription team of 93 interviews conducted with Brazilians living in Canada and their relatives living in Brazil, identified trends in the experiential learning acquired during the pandemic. The Bloom’s taxonomy framed the review of team’s insights about learned lessons and newest skills and organization of evidence within the domains of cognitive, affective, and psychomotor learning. Results: Overall, there was a significant number and diversity of evidence about new learning and successful strategies that the participants implemented that promoted opportunities for learning. Identified evidence was in the affective (n=26), psychomotor (n=11) and cognitive (n=8) domains. Learning occurred in the affective domain which contributed to new self-perception, expanded awareness, new life priorities, renewed humanistic thoughts, increased valorization of time, life, and interpersonal relations. Conclusions: The findings of the lessons learnt from Brazilian participants are significant and highlight the unique perspectives of the positive benefits that resulted from a negative experience due to the pandemic. The significance of this interesting set of evidence indicates that in a near future the multidisciplinary community of scientists may definitively recalibrate the research focus and further explore how individuals learn and react during a pandemic.
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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 | 0.019 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.028 | 0.034 |
| 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".