Factors Affecting Academic Resilience During Crises: Cases of Secondary School Students in Phuket, Thailand
Bibliographic record
Abstract
Academic resilience is crucial in today’s crisis-prone society. This qualitative study explored the factors that shaped academic resilience during the COVID-19 pandemic to strengthen the global literature on resilience and postpandemic policy and practice in education. This study adopted a multiple-case study design, with the application of replication logic and data collection via semistructured interviews. The case studies featured interviews with three academically resilient students in Phuket and nine relevant informants, including parents, homeroom teachers, and local stakeholders. These interviews covered various factors surrounding personal qualities, families, peer groups, schools and teachers, communities and cultures, and the pandemic. Through thematic analysis, seven overarching themes emerged from the data: (1) achievement-oriented characteristics, (2) high aspirations, (3) COVID-19-driven adaptability, (4) self-directed learning in the use of online resources, (5) healthy family functioning, (6) role models, and (7) social support in the context of a giving culture.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".