‘We were called guardian angels; Was that sincere? I do not think so’: retention of certified nurse assistants during the COVID-19 crisis in long-term care facilities in Montreal, Quebec, Canada
Bibliographic record
Abstract
The COVID-19 pandemic in the Canadian province of Quebec has increased demand for labour in long-term care facilities, or ‘Centre d’hébergement de soins de longue durée (CHSLDs)’. This study explored the challenges experienced by Certified Nurse Assistants (CNAs) in CHSLDs in Montreal, Quebec’s largest city, during the COVID-19 pandemic and how these challenges affected their job retention. We employed an analytical descriptive qualitative research approach, using semi-open interviews to collect data from three categories of CNAs between October and December 2021 in Montreal CHSLD. Our interview guide was based on two Canadian theoretical frameworks: one on nurses' retention and one on work-family balance. A thematic analysis method was employed to analyze the data. Our findings reveal that the vicious circle of service failure, including stress, exhaustion, and poor relationships with management and coworkers, influenced CNAs’ decision to quit their jobs. Poor working conditions, difficulty balancing work-family-personal life, and lack of appreciation for the profession all contributed to some CNAs quitting their jobs. At the same time, emotional attachment to the work and support from managers, organizations, and the government played a critical role in retaining CNAs. The CHSLDs network in Quebec, already marked by job instability, has been further weakened by the COVID-19 pandemic. Improved working conditions, respect for the CNA profession, and a recruitment process that accounts for candidates’ professional motivations are necessary to improve CNA retention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".