Addressing Staffing Shortages in Nursing Homes: Does Relaxing Training and Licensing Requirements Increase Nurse Aide Staffing?

ABSTRACT

Objective

To evaluate whether COVID‐19‐related nurse aide training and licensing relaxation policies improved staffing shortages in nursing homes.

Study Setting and Design

Staffing shortages have been a long‐standing concern in nursing homes, and states are experimenting with different approaches to enhance nurse aide staffing. We use the latest quasi‐experimental difference‐in‐differences methods to evaluate the effect of relaxing training and licensing requirements in 19 states (treatment group) relative to the 31 states that did not implement such policies (control group). We analyze the combined effect of relaxing both training and licensing requirements, as well as the impact of relaxing each policy separately.

Data Sources and Analytic Sample

We obtain quarterly data on nursing home characteristics, including adjusted nurse aide hours per resident day (HPRD) from 2019 to 2023 from Care Compare, a federal website with quality information on all Medicare/Medicaid‐certified nursing homes. After excluding outliers of staffing data (nurse aide HPRD > 5.25, or nurse aide HPRD = 0), our final analytical sample had 278,170 observations.

Principal Findings

The average nurse aide HPRD is 2.30 in the treatment group and 2.26 in the control group. Using the difference‐in‐differences regression analyses, we find no significant effect of the relaxation of training and licensing requirements on nurse aide levels (average treatment effect: −0.0001; p = 0.99). Similarly, separate analyses of training and licensing relaxation policies suggest that neither policy significantly impacts nurse aide staffing. Results are consistent when we adjust for staffing requirements, wage increase policies, and nursing home characteristics.

Conclusions

Our findings suggest that the relaxation of training and licensing requirements may not lead to improved nurse aide staffing levels in nursing homes. Policymakers need to consider other strategies to address persistent staffing shortages in nursing homes.

Summary.

• What is known on this topic

  • ○
    Nursing homes in the United States have nurse aide staffing shortages.
  • ○
    The COVID‐19 pandemic exacerbated nurse aide shortages in nursing homes.
  • ○
    From March 2020 to September 2021, federal and state authorities implemented new initiatives aimed at increasing the supply of nurse aides in nursing homes.
• What this study adds

  • ○
    Relaxing nurse aide training and certification requirements during COVID‐19 did not improve the nurse aide hours per resident day in nursing homes.
  • ○
    Policymakers need to consider alternative policies to improve nurse aide shortages in nursing homes.

1. Introduction

Nursing homes employ 4.7 million direct care workers who provide long‐term and post‐acute care to 1.2 million residents in the United States [1, 2]. Nursing staff includes registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs) [1]. Shortages of nurses are an important determinant of the quality of care in nursing homes [2, 3]. Studies show that shortages of nurses are strongly related to high morbidity and mortality of nursing home residents [4, 5].

Given the importance of staffing, staffing shortages in nursing homes have been a long‐standing issue for policymakers and researchers [6, 7]. More specifically, nurse aide shortages have been concerning because nursing homes (NHs) have struggled to fill nurse aide vacancies [3]. To maintain the quality of nurse aides, CMS and the states impose minimum training and licensing requirements, which some argue act as a barrier to entry for the profession. Thus, some states have relaxed these requirements with the goal of boosting nurse aide levels in NHs.

CNAs provide approximately 80% of direct care in nursing homes and are crucial for providing adequate care to the residents [7, 8]. With an increase in the older age population and the demand for long‐term care services, nursing homes have struggled to fill the CNA vacancies [9]. Over half of the CNAs leave their jobs within weeks of being hired or within the next 6 months [9].

The literature identifies several factors responsible for CNA shortages and high turnover rates. These include low wages, high levels of stress, and burnout [10, 11]. Advocates argue that the difficulties in receiving training and licensing requirements of CNAs also create hurdles in joining the labor force [12, 13]. Nursing assistants, commonly known as NAs (nursing assistants/nurse aides), must complete a certificate program to learn basic nursing and personal care skills, followed by a competency exam that permits them to use state‐specific titles such as CNAs [12]. While federal regulations require NAs to complete at least 75 h of initial training, states vary in their current requirements for the minimum number of hours of initial training for CNAs [12]. The majority of the states require more than the federally mandated minimum of 75 h of training for CNAs. For example, 15 states require 76–119 h, and 12 states plus the District of Columbia require 120–175 h of training [12]. Minimum requirements for the number of hours of clinical training also range from 16 to 100 h across different states [12].

These federal CNA training and oversight requirements have not changed in the last two decades [14]. Studies suggest that the federal and state‐related licensing and training requirements are strongly related to the resident health outcomes, improved professionalism, and retention rates of the CNAs [8, 13]. Although training requirements have not changed, a recent CMS mandate requires minimum staffing hours per resident day (HPRD) in nursing homes [15]. The mandate requires a minimum of 2.4 HPRD for CNAs at nursing facilities for the provision of care regardless of the case mix and bed occupancy rates of the nursing homes [15].

The COVID‐19 pandemic disrupted the healthcare system and exacerbated staffing shortages in nursing homes [9]. Even before the pandemic, 47% of nursing homes in 2018 did not meet the expected staffing levels in the United States [4]. COVID‐19 resulted in high levels of turnover due to burnout and stress, resulting in further decline in the number of employed nursing staff [11].

Nursing homes experienced a high incidence of infection among residents and staff, leading to further staffing shortages [10, 11]. COVID‐19 outbreaks were associated with a statistically significant drop in the nurse staffing levels reflected by elevated absences and departures [16]. Even though nursing homes tried to hire agency workers to boost staffing during this time, there was a 2.6% decrease in staffing hours at the peak of COVID‐19 [17].

During the first week of the pandemic, federal and state authorities received urgent calls for intervention from long‐term care facilities to address nursing shortages [14]. As part of an effort to boost the nurse aide supply, some states relaxed the training and licensing requirements of nurse aides between March 2020 and September 2021 [15, 16]. More specifically, states implemented three different types of policies: (a) reducing training hour requirements for CNAs, (b) allowing personal care attendants to become CNAs with temporary training, and (c) waiving professional/licensing requirements/renewals for CNAs [14, 17, 18]. Of the 50 states, 19 states relaxed training and licensing requirements for nurse aides to improve the nurse aide supply in nursing homes during the early part of the COVID‐19 pandemic [11, 14, 15].

The relaxation policies adopted by different states differed due to differences in shortage levels of staffing, the intensity of the COVID‐19 pandemic, state‐level regulatory and workforce differences, and the opportunity for states to adjust policies within the federal guidelines [18]. For instance, states like WI, AK, and DE required 140–150 h of CNA training compared to the federal 75‐h minimum requirement. This allowed these states to reduce training hours to boost CNA levels [19].

Compared to CNAs, personal care aides (PCAs) or temporary aides could start working with just a few hours of emergency training in some states with relaxed regulations. PCAs work under the supervision of CNAs and primarily perform non‐medical tasks such as feeding, bathing, dressing, and helping residents with mobility. In states with severe shortages of CNAs, PCAs were allowed to perform a wider range of tasks that are usually done by CNAs. While this policy choice may address the workforce shortages in times of public health emergencies if workers respond to the policy change, it also lowers the competency standards and can lead to negative impacts on the health outcomes of the residents [19, 20].

Certain states such as CA, ME, and DE have strict licensing requirements for CNAs, which include background checks and ongoing continued education/training of CNAs to renew their licenses. Relaxing these requirements was expected to increase the mobility of the workforce and lower the barriers to entry for or retention of the workers [20, 21].

COVID‐19 thus provided an opportunity for the state authorities to adopt different policy measures to help boost CNA levels in nursing homes [8, 11, 19, 20]. However, it is unclear whether such training and licensing relaxation policies increase CNA staffing in nursing homes. Given the persistent shortages of staffing in nursing homes, it is crucial to evaluate the effectiveness of these policy interventions aimed at improving staffing levels.

To this end, we evaluate the effect of relaxing training and licensing requirements on CNA levels in nursing homes. Our findings have implications for federal and state policymakers interested in expanding nurse aide levels in nursing homes.

2. Methods

2.1. Conceptual Framework

CNA staffing depends on a combination of factors at the individual, organizational, and regulatory levels [22]. Federal and state regulations, including training and quality standards, play a critical role in shaping workforce retention and stability. Stringent regulations on license renewals and higher training requirements have been a barrier for workers who want to join the labor force. The COVID‐19 pandemic intensified workforce shortages, leading state authorities and nursing home providers to adopt measures aimed at addressing these challenges. Reducing barriers to workforce entry—such as simplifying training and licensing requirements—was considered a potential strategy to improve staffing levels in nursing homes [13].

Furthermore, organizational characteristics, such as facility size and ownership type, also shape hiring practices. For instance, for‐profit facilities may reduce staffing costs to maximize profits [23]. The pandemic thus created an opportunity for state authorities to ease requirements related to CNA licensing and training, allowing us to assess whether such relaxations effectively improved CNA staffing levels in nursing facilities.

2.2. Data Sources and Variables

We obtain data on our key independent variable—training and licensing policy changes—from RTI international COVID‐19 research + response documentation and Kaiser Family Foundation (KFF) [15, 16]. We then updated the policy data to 2023 through our search of each state website for changes in nurse aide staffing policies and regulations during COVID‐19. For our predictor variables, based on our review, we identified 19 states that implemented nurse aide training and licensing relaxation policies with some differences across states. Table S1 provides details on the grouping of the treatment states based on the adopted policy variation. Treatment Group 1 represents seven states (WI, AK, DE, GA, NJ, IA, and TX) that reduced the training hours and the mode of training requirements for the CNAs (training relaxation group). Treatment Group 2 represents five states (KY, IN, MO, CT, and KS) that allow the use of personal care attendants (PCA group) as CNAs. Finally, Treatment Group 3 represents seven states (CA, CO, DC, FL, NH, NY, and VA) that waived certain professional/licensing requirements/renewals for the CNAs (licensing relaxation group). For example, some states suspended the license renewal requirements [15, 16, 21]. Different policies across different states were aimed at increasing CNA levels in nursing homes and were implemented at different times during the pandemic. We created a binary indicator for treatment equal to 1 if the nursing homes are in the 19 states with some policies aimed at expanding nurse aide levels or 0 if the nursing homes are in the 31 states without such policies (see Table S1) [15, 16, 18].

To obtain our dependent variable for analysis, we compile quarterly data on average adjusted CNA HPRD, our key outcome variable, for the treatment and control groups from Care Compare from 2019 to 2023 [22]. Care Compare is a federal website that has information on currently active nursing homes, including certain nursing and non‐nursing staffing measures, the number of beds, and other nursing home characteristics. The staffing hours reported in Care Compare are obtained from the Payroll‐Based Journal (PBJ) [23]. CMS uses staffing data from PBJ and adjusts them for case mix to report adjusted nurse aide HPRD. CMS switched to using only PBJ staffing data in the middle of 2018. To ensure that the staffing data measurements are consistent over time, we limit our analyses to staffing data from 2019 to 2023 [24]. Consistent with previous studies, we define adjusted nurse aide HPRD by including certified nurse aides (CNAs), aides in training, and medication aides/technicians [4]. Using data on staffing and other nursing home characteristics, we create longitudinal quarterly data for all nursing homes from the first quarter of 2019 to the third quarter of 2023.

2.3. Statistical Analyses

We use the difference‐in‐differences method to evaluate the effect of the relaxation of training and licensing requirements on nurse aide staffing in nursing homes. Since we have three different groups or cohorts that enacted these policies at different times (2020q1, 2020q4, and 2021q3) during the pandemic, we used the Callaway and Sant'Anna differences‐in‐differences method (C&S) that accounts for potential effect heterogeneity [25]. In the C&S method, we compared changes in CNA staffing in nursing homes that were in states that enacted relaxation policies to nursing homes in states that never enacted such policies. More specifically, the C&S method estimates all possible two‐group/period combinations and aggregates them into average treatment effects [26]. In our primary analyses, we estimate several models. First, we estimate models without any control variables by analyzing all relaxation policies together (Treatment Group 1, Treatment Group 2, and Treatment Group 3 combined). Second, we excluded the 15 states that changed CNA staffing requirements and/or introduced wage/hazard pay policies from the control group to mitigate potential bias in our estimates (see Table S2).

Third, most of the treatment states had the end of the federal public health emergency (May 11, 2023) as the standard policy termination date, but some states terminated the policy earlier (GA, IA, MO, CT, NY, and VA) [21]. To ensure all treatment states had a follow‐up time until the first quarter of 2023, we excluded states that terminated the policies earlier from the analysis (see Table S2).

Using all the two‐group/period results, we also estimated an event study over time. The event study framework helps explain the impact of an event (treatment) before and after its introduction. More specifically, it helps us understand how the impact of the policy varies with time since its first implementation through a graphical illustration of the point estimate and confidence intervals of regression [24]. We evaluated differential trends in outcomes between the treated and control groups using these event‐study graphs. We clustered errors at the state level and used p < 0.05 to determine statistical significance. We used STATA version 17 for all analyses [27].

2.4. Sensitivity Analyses

We conducted several analyses to evaluate whether our estimates are sensitive to the modeling choices or outcomes selected. Differences in facility size, occupancy rates, and for‐profit status can be associated with differences in staffing levels in nursing homes [25]. Larger facilities and higher resident levels have higher staffing needs. Similarly, the literature suggests that for‐profit facilities may prioritize cost‐efficiency in staffing decisions, which may lead to lower staffing [26]. In the C&S models, we use these variables at baseline to predict treatment adoption and use weights to adjust outcome differences.

First, we only use the number of certified beds as the time‐invariant covariate. Second, we control for a full set of covariates, including states that adopted incentive‐based policies and additional staffing requirements, number of certified beds, ownership type, and number of residents per day. More specifically, we identified 15 states that adopted other measures during COVID‐19 to enhance nursing home staffing. These measures included increasing staffing requirements in terms of HPRD (CNA HPRD) and improving financial incentives for nurse aides (wage increases/hazard bonuses). Although these initiatives were unrelated to NA training/licensing relaxation, they might influence nursing staff availability [28]. We created a dummy variable for states that amended CNA staffing requirements during COVID‐19. For example, CMS allowed flexibility in meeting the minimum staffing requirement standards during the pandemic for the five‐star rating inspection of the nursing homes. Following the new guidelines, some states reduced the staffing requirements temporarily. We create a dummy variable indicating whether staffing requirements increased or decreased (policy adopted = 1, not adopted = 0). Additionally, we introduced a dummy variable to account for states that enacted wage increases or hazard pay policies (policy adopted = 1, not adopted = 0).

Third, in addition to the combined effect of training and/or licensing relaxation policies in our main analysis, we evaluate these policies separately: (a) training relaxation policies, (b) use of personal care policy, and (c) licensing relaxation policies. Additional details regarding the selection of treatment and control groups can be found in Table S1.

Fourth, we use PBJ data to evaluate staffing levels without adjusting for case mix at both monthly and quarterly levels. In addition, we present descriptive trends for both full‐time and contractual CNA staff using PBJ data. Finally, we conducted a placebo test using RN HPRD as the outcome variable. Placebo tests are meant to assess whether the effects on the CNA levels we are observing are due to the policies or something else. The rationale is that if the policies were found to significantly affect RN staffing—an unrelated measure—it would raise concerns about the presence of confounding factors or alternative explanations influencing the results.

3. Results

3.1. Sample Description

Table 1 provides a summary of the nursing home characteristics. We had 15,684 unique nursing homes with a total of 278,170 quarter‐year observations from 2019 to 2023. Nursing homes in the states that enacted relaxation policies have slightly higher mean adjusted nurse aide hours compared to states that never enacted such policies (2.30 vs. 2.26, p < 0.01). Similarly, nursing homes in states with nurse aide relaxation policies tend to have larger bed sizes (110.13 vs. 104.61, p < 0.01), and a higher resident population (84.05 vs. 77.7, p < 0.01). Figure 1 shows a descriptive trend in adjusted nurse aide HPRD for nursing homes over time, and it suggests that states that implemented relaxation policies had slightly higher nurse aide HPRD throughout the study period compared to those that did not.

TABLE 1.

Descriptive statistics for nursing homes from 2019 to 2023.

Variables	Treatment group	Control group
	Mean (SD)	Mean (SD)
Outcome variable
Adjusted NA hours per resident day (HPRD)	2.30 (0.55)	2.26 (0.59)
NH characteristics
Number of residents per day	84.05 (54.3)	77.79 (42.5)
Number of certified beds	110.13 (65.3)	104.61 (54.3)
Ownership type (%)
For‐profit	70.9	70.6
Non‐profit	29.1	29.4
Staffing HPRD
Adjusted total nurse staffing hours	3.9 (0.91)	3.8 (0.92)
Adjusted RNs HPRD	0.66 (0.47)	0.74 (0.47)
Adjusted LPNs HPRD	0.91 (0.36)	0.88 (0.35)
Number of unique providers	7933	7751
Total number of observations	140,708	137,462

Note: A registered nurse (RN) is responsible for daily health monitoring and care of nursing home residents. A licensed practical nurse (LPN) provides patient care and works closely with RNs. A nurse aide (NA) assists residents with daily tasks such as eating, hygiene, and bathroom use. The treatment group includes nursing homes that have implemented at least one of the relaxation policies.

FIGURE 1.

Adjusted nurse aide hours per resident day (HPRD) over time. Data from 15,684 nursing homes (quarterly) depicting adjusted nurse aide HPRD in the treatment group vs. the control group.

3.2. Difference‐in‐Difference Estimates

Table 2 reports the treatment effects of nurse aide training and licensing relaxation policies (all policies combined) on nurse aide HPRD levels in nursing homes using Callaway and Sant'anna's (2021) difference‐in‐differences regression method [29]. First, we analyze a full sample of 15,684 unique nursing homes, representing the average treatment effect without any covariates, using never‐treated and not yet‐treated as controls. We find no statistically significant relationship between training, licensing relaxation policies, and nurse aide HPRD in nursing homes.

TABLE 2.

Combined effects of nurse aide training and/or licensing relaxation policies on nurse aide HPRD levels in nursing homes.

	Average treatment effect
Never treated as control group	−0.0001
Full sample	(0.017)
Excluding 15 states with additional policies	−0.015
	(0.022)
Excluding early termination states	−0.013
	(0.017)
Not yet treated as a control group
Full sample	−0.0002
	(0.016)
Excluding 15 states with additional policies	−0.013
	(0.021)
Excluding early termination state	0.0157
	(0.016)

Note: Full sample for all relaxation policies together includes 15,684 unique nursing homes between 2019 and 2023. Robust standard errors clustered at the state level are reported in parentheses. No estimates in the table reached statistical significance level at *p < 0.1, **p < 0.05, ***p < 0.01.

Second, we analyzed the relaxation policies by excluding the state that adopted additional measures to improve CNA levels in nursing homes apart from the relaxation policies. We found no impact of NA training and licensing relaxation policies on adjusted NA HRPD (β = −0.015, p = 0.49). Findings were similar to our main findings whether we included both never and not‐yet‐treated groups as controls.

Third, in our main results, we analyzed the relaxation policies by excluding the states that terminated the policies earlier. We found no impact of CNA training and licensing relaxation policies on adjusted CNA HRPD (β = −0.013, p = 0.15). Findings were similar to our main findings, whether we excluded states that terminated the program early or not, including both never and not‐yet‐treated groups as controls.

Event‐study estimates from the main model are shown in Figure 2. Both pre‐ and post‐policy estimates are compared to the quarter before the quarter during which training and licensing relaxation policies were adopted. Overall, there is little evidence to suggest that training and licensing relaxation policies had a significant impact on nurse aide levels.

FIGURE 2.

Event study—Effect of licensing and training relaxation policies on CNA HPRD. The dots represent quarterly estimates, whereas the vertical bars represent 95% confidence intervals. ATT stands for average treatment effect on the treated.

3.3. Robustness Checks

We analyzed the relaxation policies by controlling for certain covariates. Table S3 shows the treatment effect when controlling for the number of certified beds and a full set of covariates. In these models, we find no statistically significant relationship between training, licensing relaxation policies, and nurse aide HPRD in nursing homes.

We did not find significant associations between the relaxation policies and nurse aide HPRD when we separately analyzed these different training and licensing relaxation policies: training hours reduction policy, licensing relaxation policy, and use of personal care attendants' policy (Table S3). We reached similar conclusions whether we estimated unconditional models, controlled for only bed size, or controlled for a full set of covariates.

We conducted additional analysis at the monthly level using PBJ data sets to further investigate CNA HPRD. The results indicated no significant improvements in CNA HPRD in the treatment states when analyzed using monthly staffing levels (−0.009, p = 0.91).

Figures S3 and S4 show the trends in quarterly unadjusted contractual and full‐time CNAs descriptively. Data suggest no meaningful differences in the trends for contractual and full‐time CNAs in treatment vs. control states.

Finally, our placebo test results are presented in Table S5. When we evaluated the combined effect of nurse aide training and licensing relaxation policies on adjusted RN HPRD, we observed no significant associations (−0.016, p = 0.15).

4. Discussion

In our study, we evaluate the effect of relaxing nurse aide training and licensing requirements on nurse aide HPRDs in nursing homes. We find no evidence to suggest that these policies, jointly or separately, are effective in increasing nurse aide HPRDs. Our findings are largely consistent with previous research and anecdotal findings on the stagnant or worsening nurse staffing levels in nursing homes during COVID‐19 [17, 27]. Our findings that policies designed to increase nurse aide levels in nursing homes may not be effective suggest that we need to identify alternative policies to improve staffing in nursing homes.

To address staffing shortages during the COVID‐19 pandemic, nursing homes implemented other policies such as overtime pay, cross‐training, curtailing admissions, and the use of agency staff [28, 30]. While these strategies helped eliminate some strain on the facilities during the peak of the pandemic, these are not long‐term solutions. Excessive overtime work and use of agency staff can lead to staff burnout and occupational stress and ultimately result in poor nursing staff retention in nursing homes [11, 17, 28]. The use of agency staff also adds to the direct care cost quotient of the nursing homes and depletes financial resources with alternate use [7].

States need to go beyond regulatory policies if we want to improve CNA levels in nursing homes [14]. Policies aimed at improving worker satisfaction and retention are needed [7, 31].

To improve job satisfaction and retention, nursing homes can provide better training to nurse aides [8]. A recent study suggests that NAs have a higher rate of turnover because of limited benefits, low wages, and higher exposure to infections [4]. Another study found that increasing wages are associated with lower turnover of CNAs [31]. Other factors, such as better financial incentives, fringe benefits, reasonable working hours, and improved work environment, should be considered [3, 6, 8].

Previous studies have also identified nursing shortages, an increase in burnout, and rising turnover rates of nurses in hospitals and other healthcare settings during COVID‐19 [32, 33]. In certain states (AR, CA, NJ, NM, RI, and VA), regulations related to increased financial incentives and hazard pay were introduced for employees in life‐sustaining occupations, independent providers, agency‐employed home care workers, and frontline workers in all industries and professions [28]. However, evidence is scarce on the impact of these regulations on nursing shortages. Further studies are necessary to evaluate these policies and their impact on staffing.

Our analysis is subject to several limitations. First, we examine the policies implemented during the COVID‐19 pandemic, but given the public health emergency, the effect of policies such as those analyzed in this study may not have had the intended effect. We should be cautious when interpreting how broadly the results might apply in non‐pandemic situations. Second, we take advantage of the variation in state policies on training and licensing relaxation across different states, but we do not have specific interventions that individual facilities might have adopted during the COVID‐19 period. While not having individual‐level intervention data is a limitation, we do not have a basis to think that these individual interventions differed between facilities in states that adopted relaxation policies and states that did not. Yet, we attempted to account for some of the variables, such as facility size and profit status, in some of our sensitivity analyses and found no differences in effects.

Third, our estimation method is designed to address treatment effect heterogeneity across groups that implemented policies at different times, but it does not incorporate time‐varying factors that might affect outcomes. The inclusion of time‐varying factors is potentially problematic because some of these factors could be affected by the relaxation policies. However, in one of the robustness checks, we included several other controls, including the adoption of other staffing‐related policies, and our findings were similar. Fourth, we were able to evaluate the levels of nurse aide HPRD in nursing homes in our study but were unable to identify whether the relaxation policy changes affected the available supply of nurse aides. Future studies should investigate whether regulatory changes affect the supply of workers. Some of the currently working nurse aides might have been eligible to work due to new requirements. We do not have data to assess the extent to which previously ineligible nurse aides are working in nursing homes. Fifth, we focus on analyzing the impact of licensing and training relaxation policies on staffing levels. Other related policies, such as those related to wage improvement, are also important, but we were unable to include them due to differences in analytical design for such evaluations. Similarly, the policies analyzed in our study can also affect the quality of care, but we have not examined them. Future studies can explore these areas to provide a more comprehensive understanding of these policies.

In conclusion, there is little evidence to suggest that training and licensing relaxation policies had a significant impact on nurse aide levels, whether combined or separately during the COVID‐19 pandemic. While it is unclear if these policies work differently in a non‐pandemic situation, policymakers should consider multiple strategies when trying to address staffing shortages in nursing homes.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1. Supporting Information.