“I haven’t really gone through things like this”: Young long-term care workers’ experiences of working during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Long-term care (LTC) facilities were hard hit by the COVID-19 pandemic in Canada. Using life course theory concepts, we looked for conditions that led to worker moral distress -i.e. pain or anguish over not being able to take right action - and how life stage may influence experiences. OBJECTIVE: To illuminate the experiences of adults under the age of 30 who stepped into, and/or persevered in, working in LTC during the pandemic, recognizing that this emerging workforce represents the future of LTC in Canada. METHODS: This secondary analysis uses interview data from a sub-sample of 16 young workers between 18 and 29 years of age who had been working in Canadian LTC facilities for between 8 months and 7 years. RESULTS: Young workers expressed feeling guilt about mourning the loss of socially significant milestones as these milestones paled by comparison to the loss of life and consequences of resident isolation they witnessed at work. To manage feelings of moral distress, young workers attempted to maintain high standards of care for LTC residents and engaged in self-care activities. For some workers, this was insufficient and leaving the field of LTC was their strategy to respond to their mental health needs. CONCLUSION: The life stage of young LTC workers influenced their experiences of working during the COVID-19 pandemic. Interventions are needed to support young workers' wellbeing and job retention.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".