Resilience in higher education settings during the COVID-19 pandemic: A scoping literature review with implications for policy and practice
Bibliographic record
Abstract
With the onset of the COVID-19 pandemic, the construct of resilience has received growing attention in the higher education literature. The pandemic, acting as an external stressor, impacted multiple higher educational settings in 2020 during the period of lockdowns, when universities had to temporarily close on-campus activities and shift to online emergency responses. The objective of this scoping review is to explore how resilience was conceptualized in the higher education research literature during the initial emergency response phase of the pandemic, and how conceptual and research design choices in this early body of literature shaped policy recommendations aimed at enhancing resilience of individuals and support systems in higher education settings. This article, thus, contributes to the ongoing discussion in the academic and policy-relevant literature on how to better prepare universities as organizations and communities for a response not only during the emergency pandem ic, but also beyond in post-pandemic higher education settings. In particular, the paper examines five related questions, as pertaining to the early literature on the university emergency response in higher education: 1) how, and at which levels (i.e. individual, community, organization, system) was resilience conceptualized, 2) what types of research questions on resilience were being explored in this literature (i.e. determinants of resilience, or impacts of resilience), 3) how, and via which instruments, resilience was measured, 4) which factors were found to be facilitative for resilience, and 5) which factors were found to be impacts of resilience. The article synthesizes the findings of the early literature on resilience in higher education during the pandemic emergency response, and discusses important areas for further academic research, highlighting the implications for relevant support policies and interventions.
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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 | 0.021 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".