Impact of lockdown on occupational competence and values regarding the environment
Bibliographic record
Abstract
Background Since 2020, the world has been affected by the COVID-19 pandemic. To limit the spread of the virus, many countries ordered lockdowns, leading to occupational disruption. Aim The objective of this study was to evaluate the impact of lockdown on occupational competence and values. Method Participants in northern hemisphere French-speaking countries completed the Occupational Self-Assessment before and during the spring 2020 lockdown. Results Occupational competence decreased significantly during lockdown (p<0.01). People with private external access had higher competence scores during lockdown than those without (p<0.01). Also, an effect of the country of residence was found on occupational competence that differed before and during lockdown. Before lockdown, people living in Canada had a higher occupational competence score than those living in France (p<0.01). During lockdown, people living in Switzerland had a higher occupational competence score (M=58.52, SD 12.41) than those living in France (p<0.01) and Belgium (p<0.01). The value score remained the same during and before lockdown but the results of the study, in terms of the importance attached to occupations, highlight changes suggesting a reshaping of the personal value system. During lockdown, participants appeared to attach more importance to activities related to satisfaction, enjoyment and actualization at the expense of occupations relating to managing life and relationships. Conclusion Lockdown had a significant impact on people’s occupational competence and the environment within which the lockdown was experienced contributed to this perception. This recent change in occupational competence could have long-term implications, especially on the internal value system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".