Exploring the Association of the COVID-19 Pandemic on Employee Sleep Quality at a Healthcare Technology and Services Organization
Bibliographic record
Abstract
The COVID-19 pandemic led to global healthcare consequences including insomnia. This survey used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality at two time points (July 2020 and November 2020) among employees at a healthcare technology and services organization during the COVID-19 pandemic. Of the 1280 eligible employees, 251 complete responses (response rate, RR = 19.6%) in July and 108 (RR = 8.4%) in November were received and analyzed. The overall mean global PSQI scores were 7.3 ± 3.6 in July and 7.7 ± 3.6 in November 2020 (p > 0.05). There was no significant difference in any of the PSQI components or global scores between periods. Our findings indicate poor reported sleep quality among our study participants during the COVID-19 pandemic. Additional studies are needed to assess the longitudinal impact on sleep quality post-COVID-19 pandemic.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".