Exploring the Well-being of Health Care Workers During the COVID-19 Pandemic: Protocol for a Prospective Longitudinal Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Health care workers (HCWs) have experienced several stressors associated with the COVID-19 pandemic. Structural stressors, including extended work hours, redeployment, and changes in organizational mandates, often intersect with interpersonal and personal stressors, such as caring for those with COVID-19 infections; worrying about infection of self, family, and loved ones; working despite shortages of personal protective equipment; and encountering various difficult moral-ethical dilemmas. OBJECTIVE The paper describes the protocol for a longitudinal study seeking to capture the unique experiences, challenges, and changes faced by HCWs during the COVID-19 pandemic. The study seeks to explore the impact of COVID-19 on the mental well-being of HCWs with a particular focus on moral distress, perceptions of and satisfaction with delivery of care, and how changes in work structure are tolerated among HCWs providing clinical services. METHODS A prospective longitudinal design is employed to assess HCWs’ experiences across domains of mental health (depression, anxiety, posttraumatic stress, and well-being), moral distress and moral reasoning, work-related changes and telehealth, organizational responses to COVID-19 concerns, and experiences with COVID-19 infections to self and to others. We recruited HCWs from across Canada through convenience snowball sampling to participate in either a short-form or long-form web-based survey at baseline. Respondents to the baseline survey are invited to complete a follow-up survey every 3 months, for a total of 18 months. RESULTS A total of 1926 participants completed baseline surveys between June 26 and December 31, 2020, and 1859 participants provided their emails to contact them to participate in follow-up surveys. As of July 2021, data collection is ongoing, with participants nearing the 6- or 9-month follow-up periods depending on their initial time of self-enrollment. CONCLUSIONS This protocol describes a study that will provide unique insights into the immediate and longitudinal impact of the COVID-19 pandemic on the dimensions of mental health, moral distress, health care delivery, and workplace environment of HCWs. The feasibility and acceptability of implementing a short-form and long-form survey on participant engagement and data retention will also be discussed. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/32663
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".