Evaluating the mental health and well-being of Canadian healthcare workers during the COVID-19 outbreak
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the COVID-19 pandemic, healthcare systems have been under extreme levels of stress due to increases in patient distress and patient deaths. While additional research and public health funding initiatives can alleviate these systemic issues, it is also important to consider the ongoing mental health and well-being of professionals working in healthcare. By surveying healthcare workers working in Canada during the COVID-19 pandemic, we found that there was an elevated level of depressive symptomatology in that population. We also found that when employees were provided with accurate and timely information about the pandemic, and additional protective measures in the workplace, they were less likely to report negative effects on well-being. We recommend that healthcare employers take these steps, as well as providing targeted mental health interventions, in order to maintain the mental health of their employees, which in turn will provide better healthcare at the population level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it