Mental Health and Well-being of Healthcare Professionals Amid the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic posed unique challenges to healthcare professionals (HCPs) with increased risk of mental health and well-being globally. However, the psychological impact of the pandemic on the mental health and well-being of HCPs in Canada is not fully understood. This paper critically reviews broadly available literature on the mental health and psychosocial status of HCPs amid the COVID-19 pandemic in Canada. Methods: A comprehensive online search was conducted using the guidelines outlined by the Centre for Reviews and Dissemination for combining the findings of diverse primary studies within a single review. Online search was conducted through databases such as AMED (Allied and Complementary Medicine), Embase, Global Health, Ovid Healthstar, Mental Measurements Yearbook, EBM Reviews - ACP Journal Club, EBM Reviews - Cochrane Database of Systematic Reviews, Ovid MEDLINE(R) and Epub Ahead of Print, In-Process, In-Data-Review, and Google Scholar for the period between March 2020 and May 2023. Twenty-two studies met the inclusion criteria and were analyzed systematically using a thematic analysis approach to identify the main themes across studies. Results: The analysis uncovers three key themes: 1) HCPs face diverse mental health impacts during the pandemic; 2) HCPs are dissatisfied with organizational approaches to COVID-19; and 3) HCPs express concerns about personal well-being and the safety of others during the pandemic. Conclusion: These findings emphasize the need for HCPs to cope effectively with stressors for their own, their patients, and their families' well-being. Therefore, future research should prioritize the ways in which HCPs can maintain their emotional, mental, and psychological well-being.
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.
How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".