The Resilience and Mental Health Experiences of Emerging Adults During the COVID-19 Pandemic: Creating Safeguards for the Future
Bibliographic record
Abstract
There is limited research on the mental health impacts of the COVID-19 pandemic on emerging adults from diverse communities, including those with disabilities, international students, and students who identify as part of the LGBTQ2AAI+ community. A purposeful sample of seven undergraduate students, between the ages of 19 and 30, at a university in British Columbia, Canada, participated in this study. In-depth narrative style interviews were conducted via Zoom. Data were analyzed thematically and from a resilience lens framework. This study demonstrates that participants experienced a diversity of challenges, and thus engaged in differing processes of adjustment. Four protective factors were identified: (1) Positive relationships; (2) Perceived efficacy; (3) Purpose and ambition; and (4) Sense of normality. This study contributes towards the limited research base, and thus offers valuable insights, which can inform university policy makers, university administration, and public health policy makers to be better positioned to develop innovative adaptions of services and/or delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".