Exploring the occupational engagement and its impact on the well-being of young adults with self-identified anxiety and depression during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic reshaped the normality of occupation, health, and well-being and caused a shift in the overall experience of occupational engagement, beyond the act of doing an occupation. The current study aimed to explore occupational engagement and its impact on the well-being of post-secondary young adults with anxiety and/or depression symptoms in Canada during the pandemic. Using interpretive description methodology, 10 students’ perspectives on their occupational engagement and mental health during the pandemic were explored. Data were gathered from September 2022 to January 2023 through semi-structured online interviews and analyzed using the analytical processes in interpretive description and reflexive thematic analysis. The findings showed (a) occupations “were not lived to their full potential”, (b) experiencing mixed emotional states, (c) increased self-awareness, and (d) lasting impact of the COVID-19 pandemic. The affects on participants’ occupational choices, identity, and adaptation, and the perceived value, consequences, and dimensions of occupations influenced their overall health and well-being. Recognizing the impact of the pandemic on the occupational engagement of young adults can help to better understand the value of occupational science in health promotion.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".