ArticleInternational journal of nursing studies advances2025
Daily staffing trends and variation in Swiss long-term care from 2018-2023: A retrospective longitudinal analysis.
Article in International journal of nursing studies advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Workforce shortages present an ongoing challenge for long-term care service providers in Switzerland. After the COVID-19 pandemic, there is an urgent need to understand current workforce trends to strengthen the Swiss long-term care setting. Objective: We aimed to describe pre-, peri‑, and post-pandemic trends and variation of long-term care staffing. Design: We conducted a retrospective longitudinal analysis using daily administrative routine data from 2018 to 2023. Settings: A multisite long-term care facility in Switzerland with 180 beds. Methods: We applied a time-series decomposition - a method to distinguish trends from seasonal effects and residual variation - to describe trends and variation for three outcomes: a) the supply-demand-match (i.e., how did the available direct staff cover the actual care demand), b) the number of full-time absences (i.e., how many staff members were scheduled to work but were absent), and c) the number of temporary staff from an internal pool or external agency. We used means, confidence intervals, and percentages to summarize the yearly averages. Results: We linked data from 533,003 staff shifts and 387,585 resident days across the 6 study years. Overall, we observed constant variation but a decreasing trend of supply-demand match from a daily average number of staff of +6.88 [95% confidence intervals CI 6.86 - 6.90] in 2018 to -0.23 [95% CI -0.24 - -0.22] in 2023, meaning that for each year of the study, there was roughly one fewer staff member available per day for the same resident case mix. Simultaneously, daily staff absences increased from an average of 11.08 [95% CI 11.06 - 11.09] to 14.23 [95% CI 14.19- 14.27]. Absences decreased at the beginning of the pandemic (2020) but continuously increased from 2021 onwards, especially those with a duration of ≥ 1 week. As an organizational response, the number of shifts worked by temporary staff has increased from 5.5% in 2018 to 17.7% in 2023. Conclusions: We found a constant variation but a clinically noticeable downward trend of supply-demand-match from pre- to post-pandemic, largely driven by staff absences. It could not be reversed despite an increased deployment of temporary staff from both an internal pool and external agencies. We have revealed profound effects of the pandemic on an organization's ability to meet the required care demand. Healthcare policy should consider alternative reimbursement strategies to alleviate the financial burden associated with a high number of absences. Registration: not registered.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.