Perspectives of Yukon’s frontline health care workers during the COVID-19 pandemic
Bibliographic record
Abstract
The perspectives of Yukon's nurses and physicians can determine what might mitigate burnout and strengthen the response to the COVID-19 pandemic and/or future health emergencies. The study was conducted in the Yukon Territory, Canada in two phases: completion of the Copenhagen Burnout Inventory (CBI), and in-depth oral interviews. This paper will discuss the results of the interviews. A hybrid thematic analysis of 38 interviews revealed five primary themes: personal impacts; work-related effects; client effects/patient care; perceptions of the territorial response to COVID-19; and recommendations for future pandemics. The loss of social connection and burden of childcare contributed to personal burnout. Stressful work environments, increased workload, limited resources and feeling undervalued contributed to job stress and work-related burnout. Healthcare workers ascribed meaning to their roles in improving community health , which may have mitigated client-related burnout. Systemic change is needed to ensure the healthcare workforce can maintain service delivery and respond to future pandemics. The response to COVID-19 was mounted on the backs of frontline healthcare workers who made personal sacrifices and worked to exhaustion to serve their patients. As the healthcare system and its workforce recover from the pandemic, the calls to support healthcare workers must be answered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".