Impact of the single site order in LTC: exacerbation of an overburdened system
Bibliographic record
Abstract
BACKGROUND: The long-term care (LTC) sector has been at the epicentre of COVID-19 in Canada. This study aimed to understand the impact that the Single Site Order (SSO) had on staff and leadership in four LTC homes in the Lower Mainland of British Columbia, Canada. METHODS: A mixed method study was conducted by analyzing administrative staffing data. Overtime, turnover, and job vacancy data were extracted and analyzed from four quarters before (April 2019 - March 2020) and four quarters during the pandemic (April 2020 - March 2021) using scatterplots and two-part linear trendlines across total direct care nursing staff and by designation (i.e., registered nurses (RNs), licenced practical nurses (LPNs) and care aids (CAs)). Virtual interviews were conducted with a purposive sample of leadership (10) and staff (18) from each of the four partner care homes (n = 28). Transcripts were analyzed in NVivo 12 using thematic analysis. RESULTS: Quantitative data indicated that the total overtime rate increased from before to during the pandemic, with RNs demonstrating the steepest rate increase. Additionally, while rates of voluntary turnover showed an upward trend before the pandemic for all direct care nursing staff, the rate for LPNs and, most drastically, for RNs was higher during the pandemic, while this rate decreased for CAs. Qualitative analysis identified two main themes and sub-themes: (1) overtime (loss of staff, mental health, and sick leave) and (2) staff turnover (the need to train new staff, and gender/race) as the most notable impacts associated with the SSO. CONCLUSIONS: The results of this study indicate that the outcomes due to COVID-19 and the SSO are not equal across nursing designations, with the RN shortage in the LTC sector highly evident. Quantitative and qualitative data underscore the substantial impact the pandemic and associated policies have on the LTC sector, namely, that staff are over-worked and care homes are understaffed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".