Moral Distress, Mental Health, and Risk and Resilience Factors Among Military Personnel Deployed to Long-Term Care Facilities During the COVID-19 Pandemic: Research Protocol and Participation Metrics
Bibliographic record
Abstract
BACKGROUND: The earliest days of the COVID-19 pandemic in Canada were marked by a significant surge in COVID-19 cases and COVID-19-related deaths among residents of long-term care facilities (LTCFs). As part of Canada's response to the COVID-19 pandemic, Canadian Armed Forces (CAF) personnel were mobilized for an initial emergency domestic deployment to the hardest-hit LTCFs (Operation LASER LTCF) to support the remaining civilian staff in ensuring the continued delivery of care to residents. Akin to what was observed following past CAF international humanitarian missions, there was an expected increased risk of exposure to multiple stressors that may be psychologically traumatic and potentially morally injurious in nature (ie, related to core values, eg, witnessing human suffering). Emerging data from health care workers exposed to the unprecedented medical challenges and dilemmas of the early pandemic stages also indicated that such experiences were associated with increased risk of adverse mental health outcomes. OBJECTIVE: This study aims to identify and quantify the individual-, group-, and organizational-level risk and resilience factors associated with moral distress, moral injury, and traditional mental health and well-being outcomes of Operation LASER LTCF CAF personnel. This paper aimed to document the methodology, implementation procedures, and participation metrics. METHODS: A multimethod research initiative was conducted consisting of 2 primary data collection studies (a quantitative survey and qualitative interviews). The quantitative arm was a complete enumeration survey with web-based, self-report questionnaires administered at 3 time points (3, 6, and 12 mo after deployment). The qualitative arm consisted of individual, web-based interviews with a focus on understanding the nuanced lived experiences of individuals participating in the Operation LASER LTCF deployment. RESULTS: CAF personnel deployed to Operation LASER LTCF (N=2595) were invited to participate in the study. Data collection is now complete. Overall, of the 2595 deployed personnel, 1088 (41.93%), 582 (22.43%), and 497 (19.15%) responded to the survey at time point 1 (3 mo), time point 2 (6 mo), and time point 3 (12 mo) after deployment, respectively. The target sample size for the qualitative interviews was set at approximately 50 considering resourcing and data saturation. Interest in participating in qualitative interviews surpassed expectations, with >200 individuals expressing interest; this allowed for purposive sampling across key characteristics, including gender, rank, Operation LASER LTCF role, and province. In total, 53 interviews were conducted. CONCLUSIONS: The data generated through this research have the potential to inform and promote better understanding of the well-being and mental health of Operation LASER LTCF personnel over time; identify general and Operation LASER LTCF-specific risk and protective factors; provide necessary support to the military personnel who served in this mission; and inform preparation and interventions for future missions, especially those more domestic and humanitarian in nature. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44299.
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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 | 0.037 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".