Investigating the Connections Between Delivery of Care, Reablement, Workload, and Organizational Factors in Home Care Services: Mixed Methods Study

Background Home care is facing increasing demand due to an aging population. Several challenges have been identified in the provision of home care, such as the need for support and tailoring support to individual needs. Goal-oriented interventions, such as reablement, may provide a solution to some of these challenges. The reablement approach targets adaptation to disease and relearning of everyday life skills and has been found to improve health-related quality of life while reducing service use. Objective The objective of this study is to characterize home care system variables (elements) and their relationships (connections) relevant to home care staff workload, home care user needs and satisfaction, and the reablement approach. This is to examine the effects of improvement and interventions, such as the person-centered reablement approach, on the delivery of home care services, workload, work-related stress, home care user experience, and other organizational factors. The main focus was on Swedish home care and tax-funded universal welfare systems. Methods The study used a mixed methods approach where a causal loop diagram was developed grounded in participatory methods with academic health care science research experts in nursing, occupational therapy, aging, and the reablement approach. The approach was supplemented with theoretical models and the scientific literature. The developed model was verified by the same group of experts and empirical evidence. Finally, the model was analyzed qualitatively and through simulation methods. Results The final causal loop diagram included elements and connections across the categories: stress, home care staff, home care user, organization, social support network of the home care user, and societal level. The model was able to qualitatively describe observed intervention outcomes from the literature. The analysis suggested elements to target for improvement and the potential impact of relevant studied interventions. For example, the elements “workload” and “distress” were important determinants of home care staff health, provision, and quality of care. Conclusions The developed model may be of value for informing hypothesis formulation, study design, and discourse within the context of improvement in home care. Further work will include a broader group of stakeholders to reduce the risk of bias. Translation into a quantitative model will be explored.


S1. Model development supplementary
Data collection was carried out using MEDLINE/PubMed (See Table S1 for full search term). This to identify quantitative and qualitative predictors of stress in homecare, residential care, care for older people, dementia care, nursing homes and related settings. Inclusion criteria were: qualitative and quantitative studies, literature reviews, studies including homecare users and/or staff, articles in English, published during 1990-2021. Case studies and studies of administrative staff in homecare were excluded.
A preliminary screening of titles and abstracts was carried out, followed by a review of the identified publications. Data was extracted relating to relationships between variables, study design, number and type of study participants, profession and setting, instrument or protocol used, country, statistically significant quantitative relationships between variables and statistical method. The data was compiled in MS Excel (Microsoft, Redwood, WA). The dataset is available in Table S4 of the Supplementary Material.

S2. Stress model
The implemented model of distress is detailed in Figure S1. In Figure S1A boredom/underload (B) and perceived workload (W) are both presented as time-dynamic states, where control will act to alleviate W and increase B. Demand will have the opposite effect, increasing W and reducing B. B and W both have a positive effect on, increasing, distress. Figure   The behaviour of the simplified stress model was verified through theoretical simulations with arbitrarily set input parameters. Equation system 1 (Eq. 1) describes the ordinary differential equations of B and W along with their respective steady state solutions. As shown for B, the state is produced through an input of control (C) at a rate equal to C multiplied by a scalar (βBC) and the rate, kB. B is eliminated over time as a function of demand (D), the scalar βBD and rate kB,0. Similarly, W over time will depend on an input derived from D and output of C. BSS and WSS are the steady state solutions of the two states.

Equation system
(1) Figure S2 shows the reference behaviour of the model when setting input parameters to arbitrarily defined values of 1. The U-shaped relationship is recovered between distress and demand/control. The stress model was further linked to physiological/mental health variables in order to relate distress to burnout [31]. Note that values of distress and demand/control were set arbitrarily to examine the structural model behaviour. Figure S4 details the elements and connections of the causal loop diagram category organisation. Figure S4. Elements Figure S5). Tables S2 and S3 details the qualitative verification through pathway analysis against observed intervention-outcomes in nursing homes and implementation of the reablement approach in homecare, respectively.  • Increased job satisfaction. • Reduced staff turnover. + person-centred approach → + confirming homecare staff-user/family communication and relationship → + homecare user influence → + empowerment → + self-perceived health → + functional ability and autonomy → + needs met → -provision of care and services →workload →quantitative demands → + job involvement → + job satisfaction →turnover intention →job turnover + person-centred approach → + carer-care recipient/family communication and relationship → + care recipient influence → + empowerment → + self-perceived health → + functional ability and independence → + needs met →provision of care → -workload → -quantitative demands → + job involvement → + job satisfaction → -turnover intention → -job turnover + functional ability →quantitative needs, + self-perceived health → + care recipient's needs met → -delivery of care → -workload → -role conflict → -job satisfaction → -turnover intention → job turnover

S6. Model simulations
Here follows the full results from the simulation exercise. The implemented Matlab-script imported the Kumu model export file (in xlsx-format), extracted elements and connections, and allowed iterative activation of elements from a specified element of origin in the model.
Connections were scaled at a prespecified coefficient. For the purpose of this analysis all elements were set to a baseline value of zero, the coefficients of the connections were set to an absolute coefficient of 0.7, +0.7 for all positive connections and -0.7 for all negative connections. Equal weighting was assumed for all connections. The simulations were carried out over ten iterations. The implemented simulation algorithm is available as a pdf-file in the Supplementary Material.
The impact of activating 'person-centred care' (the reablement approach) is shown in the heatmap, Figure S7, and across iterations, Figure S8. Figure S9 and Figure S10 details the impact of activating the element 'workload' on remaining elements of the causal loop diagram. Figure S11 and Figure S12 show the impact of activating 'homecare staff-user adoption of technology' on remaining elements of the model. Finally, Figure S13 and Figure   S14 provide the same information for the activation of the element 'distress'.