Introduction

Working in long-term care (LTC) can be physically and psychologically demanding [1,2]. Staff in LTC are primarily unregulated care aides who are predominantly women with little, if any, formal qualifications [3]. They often have limited access to continuing education [4]. Despite providing most of the hands-on care to residents, care aides are not always viewed as essential members of the care team. Research reveals that care aides are often marginalized in the workplace [5,6]. Their care work is demanding and complex, and yet they have very few opportunities and limited autonomy to make decisions pertaining to the care they provide [3]. Banerjee and colleagues [5] describe the work of care aides as occurring in highly regimented environments, where they are expected to follow strict routines and often struggle to “balance the tension between bureaucratic requirements and the immediate, individual needs of elderly residents” (pg. 29). Under such conditions, care aides report high levels of workplace stress [4,7,8] and emotional exhaustion [9,10].

Care aides attend to people with complex health and social needs, including high levels of chronic health conditions, physical disabilities, and cognitive impairments [11]. Despite an increase in the complexity of residents’ profiles over recent years, staffing levels have remained largely unchanged to meet the increased demand for care [12,13]. It is under these increasingly dire conditions that care aides are reported to have a maximum of 3 uninterrupted minutes to complete many of their care tasks [14], often leading to rushed or missed care [15]. This care is provided predominantly (more than 80%) by care aides [16]. There are reports of a prevailing LTC culture characterized by rigid policies, prescriptive practices, and hierarchical labour conditions. Care aides often have little or no autonomy in their work [11,17,18]. This can lead to significant challenges when interacting with residents’ families, whose expectations of care often exceed what is feasible within the organizational constraints [19,20]. There is widespread concern that the highly regulated nature of LTC does not support quality, person-centered care and that time pressures direct care aides to react and respond to residents in routinized ways [21,22]. Canadian researchers report that prescriptive LTC regulations impact frontline workers’ abilities to engage in teamwork, integrate health and social care, and provide compassionate and supportive care [21,23].

Two recent systematic reviews report high levels of job-related stress among those working in LTC [24,25]. Antecedents to job-related stress in care aides include contextual factors such as time pressures [26], low social capital [24], and unrealistic work demands [27–30]. According to Statistics Canada’s most recent analysis, a heavy workload is the most common predictor of employees’ levels of job-related stress [31]. Workers employed in health and social care were more likely than the average worker to describe a heavy mental workload (32.3%, compared with 23.7% on average) and a heavy emotional load (21.4%, compared with 11.7% on average) in their work. While much is known about the antecedents to stress and people’s subjective experiences of job-stress in LTC, there has been limited research to quantify the mental workload of that work. Care aides’ physiological responses to the physical and emotional demands of their work are an important factor to consider in quantifying their mental workload in LTC environments. Understanding how care is enacted in practice and staffs’ physiological responses to work-related events is necessary to address the conditions of care aides’ work. Measures of mental workload and the impact of everyday care practices in LTC could potentially play a significant role in identifying when and where measures can be introduced to support care staff and ultimately facilitate quality resident care.

This protocol for research into mental workload via physiological measures leverages simulation as a research methodology. The LTC environment presents unique challenges for researchers attempting to study the work conditions of care aides. For instance, residents are typically frail and observing their care presents complex ethical challenges that researchers must overcome, such as fluctuating capacity to provide consent [32]. Resident safety can also be easily compromised by the presence of an outside observer, and introducing data collection measures can alter staff-client interactions. By reproducing real-world phenomenon, simulation allows for the exploration of questions that are otherwise inaccessible in a clinical setting [33]. A high degree of validity can be established in simulated research environments that reflect the clinical setting.

Design and methods

This study will be a mixed-methods study that aims to collect qualitative data about participants’ experiences of working with residents who resist care in addition to physiological measurements as proxies for mental workload. In the quantitative section of the study, we will employ an exploratory cross-sectional observational study that relies on repeat measures from each participant. We will be seeking to identify the mental workload of care aides attempting to perform routine care with a resident who forestalls care progression. Care aides will take part in a standardized simulation involving caring for a resident with cognitive impairment who resists their attempts to progress the care encounter. Each participant will go through a simulation individually. Data collection is expected to take approximately 60 minutes and will include: i. pre-simulation introduction, consent, and questionnaires; ii. application of data collection equipment to obtain physiological measures; iii) a control phase; iv) baseline data collection; v) engagement in the simulation; and v) a qualitative interview about the experience and similar work experiences.

The script for the simulation was carefully crafted to produce a simulation with a high degree of fidelity. Fidelity in simulation is “the degree of realism created through the selection of simulation equipment, setting, and scenario” [43]. There are three types of fidelity: conceptual, psychological, and physical. Conceptual fidelity is the degree to which the simulated problem or situation mirrors situations that occur in the clinical context being simulated. In this study, a high level of conceptual fidelity will be achieved because we obtained feedback on the simulation script from experts in the clinical setting [44,45], including experts in long-term care, dementia, and simulation. Psychological fidelity is the degree to which participants feel the simulation captures a real-life scenario [43]. Psychological fidelity will be achieved by hiring an actress to play the role of a resident with cognitive impairment, and others to play the role of LTC supervisors. We have trialed our planned simulation and elicited feedback from individuals who work in long-term care; small refinements to our scenario were made based on their feedback. Physical fidelity refers to the “level of realism provided by the physical elements of the simulation” (Harris et al., 2020) and will be achieved because this research will be conducted in a LTC simulation lab that is designed to the exact specifications of a LTC home. The lab mirrors a LTC bedroom and bathroom, complete with mechanical lifts, care plans, and a supply room (see S1, S2 and S3 Figs for photos).

The simulation will be validated through a combination of methods that assess both the accuracy of the simulation and the performance of the actor. The feedback received from subject matter experts (previously described) was used to refine the scenario and its authenticity, ensuring that it accurately reflects care aides’ real-world situations. The actor will receive considerable training prior to the study, including orientation to simulation in general, the research specifically, and the role to be portrayed. A consistent actor is hired for the study and simulation “run-throughs” or rehearsals have been done prior to data collection. The actor has been provided feedback on their rehearsal performances by non-participant LTC staff and members of the research team with a focus on consistency, realism, and authenticity. When the study commences, participants will take part in a debriefing session after each simulation where they will be asked to provide feedback on how effective the simulation reflected real-world caregiving activities. During the study’s pilot, this feedback will be used to refine the simulation to enhance its realism and authenticity.

A pilot test of the simulation has been conducted which resulted in the refinement of the original research design. The pilot demonstrated insufficient time planned to calibrate baseline heart rate. This finding resulted in the addition of a baseline phase where participants will be asked to perform a menial activity with low physical and mental workload demands (gather supplies from a cart). The pilot demonstrated some variability across participants in the time spend in specific phase of the simulation. To ensure consistency, a timed distraction was built into each simulation to trigger the actor to progress to the next phase (as described below). The pilot also highlighted the need to adjust how the computer used for the pupillary data was carried by participants. The initial plan for participants to wear a backpack with a laptop connected to the goggles via a USB cord proved problematic, as the laptop overheated inside the bag. To address this issue, we sourced a laptop harness that could be comfortably worn like a backpack and provided adequate ventilation, preventing the laptop from overheating during the simulation.

The simulation centers on a resident living with dementia who stalls the progression of care in three progressively escalated interventional phases, with accompanying external time pressures. The study will begin with a control phase and a baseline data collection phase. Both phases will be included to establish a foundation for comparison, isolating the physiological responses associated with rest (control) and low mental workload (baseline) from those observed during the progressively escalated interventional phases

Control phase. During the control phase participants will sit for two minutes without gross motor movement and without reading material. During this period their heart rate variability will be measured using the attached sensors (see Measures section for specifics). This phase provides data about the participant’s heart rate variability and pupil dilation in a period of rest, to serve as a comparison for data obtained during phases 1–3 of the study. Following the control phase, care aides will be provided with the resident’s care plan and left alone to review for 3 minutes. After this period they will be escorted to a storage room set up to simulate a clean supply room by an actor playing a Registered Nurse (RN).

Baseline phase. During the baseline phase, participants will be asked to perform a primarily physical task with a low mental workload profile in the supply room. Namely, they will be asked to load towels and facecloths onto a cart to bring with them into the resident’s room. This baseline period will allow us to capture normal variations in the participants’ heart during movement associated with low mental workload.

Calibration phase. Once outside of the resident’s room, the participant will be led through a series of movements as part of a simulated stretching program. These movements include exercises that emphasize shifting weight from side to side, moving weight forward and backward, and crouching down. These movements provide an opportunity to calibrate the pupil dilation monitors in a room that has been calibrated to emit the same number of lumens as the resident’s room, ensuring consistent brightness throughout the space.

Phase 1. Once stretches are complete, the actor playing the RN will point to the resident’s door and instruct the care aide that the resident is sitting in her chair beside the bed. The simulated resident immediately begins forestalling care by attempting to engage the participant in other activities (e.g., coloring and talking about her special interest in horses). This phase lasts 2 minutes to ensure a sufficient sample.

Phase 2. An external time pressure will be applied in the form of the RN actor knocking on the door and checking in on progress. The RN will emphasize to the care aide that the resident’s family is on their way into the home and that the resident must look and smell good by the time they arrive. During this period, the simulated resident will continue to resist and will verbally escalate their resistance by talking negatively about staff. This phase lasts 2 minutes to ensure a sufficient sample.

Phase 3. The resident will concede to care if it is done in their bed and move toward the bed to progress the care encounter toward hands-on care provision. Once in the bed, the resident will become increasingly verbally escalated, shake her cane, and thrown a soft object (a teddy bear) at the care aide. This phase will last 2 minutes to ensure a sufficient sample.

Phase 4. An external time pressure will be applied when the RN returns to find the resident continues to be dressed in their night clothes, lying in bed. The RN will criticize the care aide and note that the family is very difficult to interact with and has higher expectations of staff than other families. The care aide will be instructed to hold the resident’s hands while the RN performs the care work. The simulation will end after two minutes in this phase, to ensure a sufficient sample.

Timeline

Recruitment of the participants began in late fall 2024. All participants have been recruited, and simulations are expected to be completed by May 2025. The data analysis will be conducted by September 2025 and the manuscript will be completed by December 2025.

Conclusion

Despite their essential role, care aides have been largely overlooked in healthcare research [62]. This lack of attention not only undermines their contributions, but also limits how we can help improve their working conditions, address their unique challenges, and ensure their well-being. Without a clear understanding of the impact of the work on their well-being, it is difficult to develop effective practices to support them and it is challenging to provide adequate training. This oversight perpetuates issues such as burnout, high turnover rates, and insufficient recognition, ultimately impacting the quality of care they provide to residents.

This study will produce high-quality, rigorous data on the physiological impact of providing care for residents in LTC under time pressures. To our knowledge, this is the first study that will examine the physiological responses of care aides to their everyday work demands, albeit during a simulation. This research promises to extend existing knowledge on care aides’ experiences and provide a deeper, multidimensional understanding of stress and mental well-being in caregiving roles. This innovative approach will not only validate qualitative insights but also uncover new dimensions of care aides’ work that may have been overlooked, paving the way for more targeted interventions and support strategies in the workplace. This research also has the potential to examine aspects of LTC that have traditionally been unattainable. Simulation offers an opportunity to re-create the conditions in which care aides work. In the context of this research, simulation will allow a clinical investigation to be conducted without entering LTC. It will enable our team to explore care aides’ responses to potentially stressful encounters in a safe way that can be terminated at any point without disrupting resident care. It can also be done without unnecessarily exposing residents to potential harm from time-pressured care for research purposes. Simulation will allow researchers to continue their efforts by reproducing real-world phenomena to answer clinical research questions. Simulation offers the potential to transform how clinical research is conducted in LTC, including how new knowledge is generated and tested.

Supporting information