Occupational disruptions during lockdown, by generation: A European descriptive cross-sectional survey
Bibliographic record
Abstract
Introduction The periods of lockdown during 2020 led to changes in daily occupations. As participation relies on dynamic interactions between the person, his/her occupations and his/her environment, we wondered whether people from different generations shared the same perception of occupational disruptions during the lockdown. Methods We performed an online survey based on the Canadian Occupational Performance Measure (COPM) of adults in 27 European Union countries, the United Kingdom and Switzerland. Three groups were compared: young adults (YAs, aged 18–39), middle-aged adults (MAs, aged 40–59) and older adults (OAs, aged 60 and over). Results 2865 participants (YAs: 47%; MAs: 33%; OAs: 20%) reported a total of 6549 disrupted occupations. The most frequently disrupted domain was leisure (83%), followed by productivity (16%) and self-care (2%); there were no significant intergroup differences ( p = 0.18). In a multivariate analysis, socializing disruptions were more likely to be associated with younger age (adjusted odds ratio (OR) [95% confidence interval (CI)] = 0.62 [0.50–0.76] for YAs versus MAs and 0.46 [0.30–0.71] for YAs versus OAs. Conclusion With the exception of socializing, the main disrupted occupations were similar from one generation to another. Our findings might enable the more accurate assessment of the risk of occupational disruption in a restrictive environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".