Adopting new habits and routines in response to COVID-19 lockdown disruptions: A qualitative study
Bibliographic record
Abstract
Introduction: COVID-19-related restrictions resulted in changes to time use and occupational participation, impacting individual and collective well-being. This study addressed a knowledge gap concerning the adaptive process during periods of occupational disruption. We explored the experience of occupational disruption and how people managed disruption during the COVID-19 pandemic. Methods: We used a qualitative descriptive approach and interviewed 18 participants of a larger survey study of time use during the COVID-19 pandemic undertaken around a medium-sized city in Canada. Transcript analysis was conducted inductively using conventional content analysis. Findings: . In the face of disruption, participants described a sense of loss and disconnection, and challenges with time management. Establishing new habits and routines required new learning associated with increased time and flexibility, connecting with others and health and wellness. Conclusion: During changing pandemic restrictions, participants expressed a sense of loss, disconnection and time management challenges associated with occupational disruptions, but also described ways they adapted, improving their health and well-being. Strategies identified through this work may be used to enhance adaptation during disruptions. Future research should explore differences in adaptation, among more diverse populations.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".