Trials and tribulations among members of Canada’s Defence Team early in the pandemic: key insights from the COVID-19 Defence Team Survey
Bibliographic record
Abstract
INTRODUCTION: Due to the unprecedented impact of COVID-19, there is a need for research assessing pandemic-related challenges and stressors. The current study aimed to assess key concerns and general well-being among members of Canada's Defence Team, including Canadian Armed Forces personnel and members of the Department of National Defence (DND) Public Service. METHODS: The COVID-19 Defence Team Survey was administered electronically to Defence Team staff in April and May of 2020 and was completed by 13 688 Regular Force, 5985 Reserve Force and 7487 civilian DND Public Service personnel. Along with demographic information, the survey included assessments of work arrangement, pandemic-related concerns, general well-being and social and organizational support. Weighted data (to ensure representation) were used in all analyses. RESULTS: The majority of respondents were working from home, with a small minority unable to work due to restrictions. Though many concerns were endorsed by a substantial proportion of respondents, the most prevalent concerns were related to the health and well-being of loved ones. The majority of respondents reported their partner, family, supervisors, friends, colleagues and children provided general support. Half of the civilian defence staff and one-third of military respondents reported a decline in mental health. Women, younger respondents, those with dependents and, in some cases, those who were single without children were at risk of lower well-being. CONCLUSION: The pandemic has negatively impacted a substantial portion of the Defence Team. When responding to future crises, it is recommended that leaders of organizations provide additional supports to higher-risk groups and to supervisors who are ideally positioned to support employees during challenging times.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".