Work Shift Research Articles

Application of Music Therapy in Improving the Sleep Quality and Mental Health of Nurses with Circadian Rhythm Sleep Disorders Caused by Work Shifts.

To analyze the impact of using music therapy to improve the sleep quality and mental health of nurses who suffer from circadian rhythm sleep disorders. Our retrospective analysis included 360 nurses with circadian rhythm sleep disorders from 20 different departments who worked in Wuhan Third Hospital for more than 1 year from May 2022 to May 2023. A total of 180 nurses with circadian rhythm sleep disorders were classified as the control group (received melatonin receptor agonist Melatonin Receptor Type 2 [MT2]). The other 180 cases were classified into the observation group (received melatonin receptor agonist MT2 + music therapy). After six courses of treatment, the clinical efficacy, Self-Assessment Anxiety Scale (SAS) score, Self-rating Depression Scale (SDS), and Pittsburgh Sleep Quality Index Scale (PSQI) score of the two groups of nurses were compared. Before treatment, no statistically significant differences in baseline treatment and SAS, SDS, and PSQI scores were found between the two groups. After treatment, the total effective rate of treatment in the observation group was 85.56%, which was significantly higher than the 50.56% in the control group (P < 0.001). After treatment, the SAS, SDS and PSQI scores of the observation group were lower than those of the control group (P < 0.05). The SAS, SDS and PSQI scores of both groups were lower after treatment than they were before treatment (P < 0.05). Implementing music therapy for nurses with circadian rhythm sleep disorders caused by shifts can improve sleep quality and reduce negative emotions. It is a clinical strategy with clinical application value and a certain promotion value.

Factors influencing nurses' practice of skin-to-skin contact as a pain management intervention for preterm infants

PurposeTo identify contextual as well as individual (personal and professional) factors influencing the practice of skin-to-skin contact (SSC) by nurses as a pain management intervention for preterm infants. SSC is among the most effective non-pharmacological interventions to manage procedural pain in preterm infants. MethodsIn this secondary analysis of a comparative international cross-sectional design study, 202 nurses were recruited and completed self-administered questionnaires. A mixed model was conducted. ResultsThe contextual factors correlated with the use of SSC for pain management included the country (p < 0.001) and work shift (p < 0.001). Individual nurse factors were level of education (p < 0.001), motherhood history (p < 0.001) as well as self-reported skin-to-skin practices [p < 0.001] (positive attitudes, education, and implementation). ConclusionMany factors may contribute to explain the differences in the use of SSC for pain management by nurses. These factors could be of relevant to consider when implanting SSC to manage procedural pain in preterm neonates.

Impact of Fatigue, Sleep Quality, and Drowsiness on Medical Laboratory Technologists’ Alertness During Night Shift

Medical Laboratory Technologists (MLTs) often work in shifts, with night shifts being much longer than morning or day shifts. As a result, many MLTs are exposed to high levels of fatigue, sleep problems, and drowsiness, while MLTs are always required to be alert for emergency analysis. This research aims to determine the impact of fatigue, sleep quality, and drowsiness on decreased alertness of MTLs working night shifts. Additionally, it describes the fatigue, sleep quality, drowsiness, and decreased alertness of MLTs. The method of analysis used was multiple linear regression. The research found that most MLTs working night shifts suffer from high fatigue levels, poor sleep quality, drowsiness, and decreased alertness. The findings suggest that fatigue and drowsiness have an impact on decreased alertness, while sleep quality has no impact on decreased alertness. These findings have implications for work scheduling and managing fatigue and drowsiness among MLTs.

STUDYING THE IMPACT OF DUST EXPOSURE AND ASSESSING THE RISK TO EMPLOYEE HEALTH

Purpose. This article is dedicated to the experimental determination of the level of dust pollution during ore crushing and the study of the long-term impact of dust exposure on workers, as well as the assessment of their health risk. Design/methodology/approach. To achieve this goal, we carried out full-scale measurements of dust pollution in real working conditions, performed mathematical modelling to analyse the data and predict the dust load over a certain period, and calculated the conditional risk to employees. Conclusions. As a result of the experiment, it was found that during the operation of the crusher, the concentration of dust in the air significantly exceeds the maximum permissible concentration (2 mg/m3). A mathematical dependence describing the dynamics of changes in the concentration of dust in the air was obtained. The average dust load per worker during a work shift and the average daily dust load, the conditional risk of an inhalation occupational disease in workers, the dust load per worker for different periods of time depending on the use/non-use of a respirator are calculated and it is proved that the use of PPE significantly reduces the impact of dust load on the worker. Limitations/consequences of research. The proposed methods for calculating conditional risk and predicting dust exposure for workers are universal for those professions in which there is significant dust pollution in the workplace. Practical consequences. The results of the study can be used to calculate and assess the dust exposure of employees during different periods of work, which will help to select the optimal personal protective equipment for employees. Originality/meaning. For the first time, an integrated method has been applied, which includes field studies of dust pollution in the workshop of a business entity, namely a mining and processing plant, mathematical modelling with modern applied software tools and, accordingly, a reasonable prediction of the consequences of long-term exposure to dust load, taking into account the conditional risk of respiratory disease.

Examining the effect of maintenance work on employees’ physical and mental health, as well as their overall well-being, in industrial settings

This study examines the impact of maintenance work on the physical, mental health, and overall well-being (PMHOW) of employees in some industry types in South Eastern Nigeria. The research aims to enhance occupational health standards and support interventions for industrial maintenance workers. It identifies the stressors that maintenance workers face (physical work demands, high-pressure work environment, shift work and irregular work hours, lack of recognition and support, exposure to hazardous conditions, job complexity and technical challenges, and; monotony and repetitive tasks), these stressors affect employees’ health and well-being through musculoskeletal disorders, mental fatigue, increased risk of accidents, stress, anxiety, and burnout to mention but a few, it proposes strategies for mitigating them which include ergonomic practices, realistic workloads, supportive work culture, adequate staffing etc. Employee Satisfaction Survey was conducted across targeted industries. Based on survey results, employees' perceptions of variances in work characteristics reveal a mix of stable and fluctuating trends. This information helped identify which work characteristics were consistently experienced and which ones vary significantly among employees. Euclidean Distance assessment carried out also aided in identifying areas of intervention by focusing on specific groups of related work characteristics to enhance the work environment and promote better health outcomes. To enhance the mental and physical well-being of maintenance workers, organizations should implement comprehensive strategies that are tailored to the unique stressors and challenges of their work. This approach will not only boost job satisfaction but also contribute to a more efficient and healthier workforce.

A Cross-Sectional Study to Analyze the Physical and Cognitive Fatigue Due to Sleep Disruption Among Shift Workers in Tamilnadu

The objective of this research is to analyse the extent and manner of the kind of fatigue among shift workers in Tamil Nadu, India. As for shift workers, they often have disturbed night’s sleep. Shift work is distinguished by its irregular and unique work hours, unlike the regular day-time Schedule. It is combined with circadian alteration, which affects most of the bodily activities. They have diverse sorts of health problems, such as physical and mental fatigue. A Cross-sectional Analysis was done among airport workers. 200 participants were randomly selected for the study aged 35-45 years and conducted for a duration of 6 months in the Airport, Tamilnadu. Sleep quality and cognition were assessed using a validated questionnaire. The muscle fatigue was evaluated using Mosso’s ergograph. R-statistical Software (version 4.0.2) was used for Data Analysis. The Primary analyses of data set’s normality were done by Kolmogorov - Smirnov Z test. Significant negative correlation exists between global PSQI score and both cognition score (r= -0.315, P&lt;0.001) and Work done by Mosso (r= -0.405, P&lt;0.001) There is also negative correlation exists between work done and the cognition score (r= - 0.565, P&lt;0.001) which is statistically significant. Decreased Sleep Quality, more fatigue and lowering of cognition levels not only affects physical health but also the quality of life. It has been noted that sleep disturbance leads to physical and mental exhaustion among Tamil Nadu shift workers. The outcomes underscore the need for targeted interventions to improve sleep-related behaviours as well as the treatment of fatigue in this population. Reducing the disturbances of shift work effects on produce, growth as well as health may be authorized by way of implementing organized work shifts and also promoting sleep promotion.

Power saw noise levels during steel stud cutting tasks on commercial construction sites: a tool characterization from a worker exposure standpoint.

Construction framers who cut and install steel studs as part of their daily tasks are exposed to hazardous noise levels during their work shift in large part due to the power saws they use to cut steel studs. This investigation characterized the sound pressure levels of power saws used to cut steel studs on active construction sites. Further, the length of time it took to cut various studs on a construction site was investigated to understand worker exposure times to saw noise. In general, power saws used on the study sites to cut steel studs had a mean A-weighted equivalent continuous sound pressure level (LAeq) of 107.2 dB and a C-weighted peak sound pressure level (LCpeak) of 120.1 dB. Three of the saws-the chopsaw, the cut-off saw, and the grinder-had similar noise levels, whereas the cordless circular saw had higher noise levels. It took an average of 13.2 s to cut each stud, and workers in the study used power saws to cut steel studs for an average of 371.5 s per day. This average exposure time at the average recorded sound pressure levels (SPLs) suggests these saws can increase the risk of occupational noise-induced hearing loss, according to National Institute for Occupational Safety and Health (NIOSH) recommendations.

Shift Work System Analysis of Store Employee Fatigue Using the Subjective Self Rating Test and Independent Sample T Test Methods (Case of Sports Station Plaza Surabaya)

This study analyzed the initial symptoms of fatigue and measured the level of fatigue with the subjective self rating test method as the subjective method, the CVL (Cardiovascular Load) method as the objective, and the independent sample t test at the Plaza Surabaya sports station. shop. Shift work in the workshop has the potential to pose health risks in the form of fatigue due to differences in physical workload experienced in each shift. Sports Station Plaza Surabaya outlets face work activity problems that are at risk of ergonomics, with several worker complaints due to work shifts that only do rotating shifts per month and unclear employee jobdesk placements. The Subjective Self Rating Test method is used to identify early symptoms of employee fatigue by filling out a questionnaire, the CVL method is used to measure pulse to obtain the level of physical workload fatigue experienced by employees. While the independent sample t test test is to find out whether there is a significant difference in the level of shift fatigue at the Sports Station Plaza Surabaya store. get the highest classification value in shift 1, for the independent t test test method get a significance value of 0.01 which means there is a significant difference in the level of worker fatigue in shift 1 and shift 2.

Precision Workforce Management for Radiographers: Monitoring and Managing Competences with an Automatic Tool.

Optimizing work shifts in healthcare is crucial for maintaining high standards of service delivery and fostering professional development. This study delves into the emerging field of skill-oriented work shift optimization, focusing specifically on radiographers within the healthcare sector. Through the development of Skills Retention Monitoring (SRH), this research aims to enhance skill monitoring, workload management, and organizational performance. In this study, several key highlights emerged: (a) Introduction of the SRH tool: The SRH tool represents a resource-efficient solution that harnesses existing software infrastructure. A preliminary version, focusing on the radiographers' professional profile, was released, and after several months of use, it demonstrated effectiveness in optimizing work based on competency monitoring. (b) The SRH tool has thus demonstrated the capacity to generate actionable insights in the organizational context of radiographers. By generating weekly reports, the SRH tool streamlines activity management and optimizes resource allocation within healthcare settings. (c) Application of a Computer-Assisted Web Interviewing (CAWI) tool for pre-release feedback during a training event. (d) Strategic importance of a maintenance and monitoring plan: This plan, rooted in a continuous quality improvement approach and key performance indicators, ensures the sustained effectiveness of the SRH tool. (e) Strategic importance of a transfer plan: Involving professional associations and employing targeted questionnaires, this plan ensures the customization of the tool from the perspective of each profession involved. This is a crucial point, as it will enable the release of tool versions tailored to various professions operating within the hospital sector. As a side result, the tool could allow for a more tailored and personalized medicine both by connecting the insights gathered through the SRH tool with the right competencies for healthcare professionals and with individual patient data. This integration could lead to better-informed decision making, optimizing treatment strategies based on both patient needs and the specific expertise of the healthcare provider. Future directions include deploying the SRH tool within the Pisa hospital network and exploring integration with AI algorithms for further optimization. Overall, this research contributes to advancing work shift optimization strategies and promoting excellence in healthcare service delivery.

A Machine Learning and Optimization Framework for Efficient Alert Management in a Cybersecurity Operations Center

Cybersecurity operations centers (CSOCs) protect organizations by monitoring network traffic and detecting suspicious activities in the form of alerts. The security response team within CSOCs is responsible for investigating and mitigating alerts. However, an imbalance between alert volume and available analysts creates a backlog, putting the network at risk of exploitation. Recent research has focused on improving the alert-management process by triaging alerts, optimizing analyst scheduling, and reducing analyst workload through systematic discarding of alerts. However, these works overlook the delays caused in alert investigations by several factors, including: (i) false or benign alerts contributing to the backlog; (ii) analysts experiencing cognitive burden from repeatedly reviewing unrelated alerts; and (iii) analysts being assigned to alerts that do not match well with their expertise. We propose a novel framework that considers these factors and utilizes machine learning and mathematical optimization methods to dynamically improve throughput during work shifts. The framework achieves efficiency by automating the identification and removal of a portion of benign alerts, forming clusters of similar alerts, and assigning analysts to alerts with matching attributes. Experiments conducted using real-world CSOC data demonstrate a 60.16% reduction in the alert backlog for an 8-h work shift compared to currently employed approach.

Working Hours, Shift, and Remote Work by Industry and Occupation in U.S. Full-time Workers.

This study examines working hours, shift work, and remote work patterns by occupation and industry among U.S. full-time workers, along with the association between working and sleeping hours. Utilizing data from 2011 to 2020 American Time Use Survey, this national household survey examines how individuals aged 15 years or older in the United States spend their time within a 24-hour period. In 2011 to 2019, U.S. full-time workers averaged 8.1 hours of work and 7.8 hours spent sleep on workdays, increasing to 9.6 hours on non-workdays. Among all occupations, Emergency medical technicians and paramedics had the longest average working hours (10.4 hours). Protective services occupations had the highest percentage (41.7%) of workers reporting often working shifts other than daytime. Among all industries, truck transportation industry had the longest average working hours (9.2). Food services and drinking places industries had the highest percentage (28.6%) of workers reporting often working shifts other than daytime. Working hours showed a negative association with sleeping hours. In 2020, 34.0% of full-time workers reported remote work due to COVID-19, with the largest percentage (72.3%) occurring in business and financial operations. The study findings offer essential benchmarks for comparing working hours and schedules across diverse occupations and industries. These insights empower occupational health practitioners to advocate for prevention measures, addressing health concerns arising from prolonged working hours and shift work.

Fragrance chemicals in nail salons: Personal inhalation exposures and potential sources

The indoor air of nail salons is full of volatile organic compounds (VOCs), many of which have fragrances. Little is known about the fragrance chemicals in nail salons, as fragrance ingredients are not required on nail product labels and are considered trade secrets. This study aimed to identify fragrance chemicals and their potential sources and exposures in nail salons. Indoor air samples were collected in seven nail salons in the Greater Boston Area between November 2016 and June 2017. Personal samples were also collected from ten nail salon workers during their work shifts. Follow-up area sampling was performed in two salons one year after the initial visits. All air samples were collected using thermal desorption (TD) tubes and analyzed on a TD-gas chromatography/mass spectrometry (GC/MS) system targeting 55 fragrance chemicals. Eighteen compounds were detected in air samples, including terpenes, alcohols, carbonyls, ethers, and esters. The concentrations displayed limited spatial variation within a salon but moderate variation over time. The highest median personal inhalation concentrations were benzaldehyde (36.4 μg/m3), 2-ethylhexanol (30.0 μg/m3), d-limonene (16.6 μg/m3), and 2-butoxyethanol (12.6 μg/m3). Highest personal levels were reached by maximum concentrations of 2-butoxyethanol (<1611 μg/m3)), d-limonene (<413 μg/m3), and methyl salicylate (<113.5 μg/m3). Personal concentrations of most compounds were highly correlated with area concentrations (Spearman correlations = 0.69−0.92). Fragrance concentrations from area and personal air samples did not correlate significantly with the ventilation rate. Cleaning agents, personal care products, and nail products were identified as important possible emission sources. This study reveals a subset of fragrance chemicals in nail salons’ indoor air and calls for future research on a full spectrum of these chemicals, their health effects among nail salon workers, and ways to reduce these exposures.

Commuting time, working time, and their link to insomnia symptoms among Korean employees: A cross-sectional study

ObjectiveAlthough commuting time is an extension of working hours, few studies have examined the relationship between commuting time and insomnia symptoms in relation to working time. Thus, this study investigated the relationship between commuting time and working time and their link to sleep disturbance. MethodsThis study included employees with ≥35 weekly working hours (n = 30,458) using data from the Sixth Korean Working Conditions Survey conducted in Korea between October 2020 and April 2021. The association between commuting time (≤60, 61-120, and >120 minutes) and insomnia symptoms based on working hours (35-40, 41-52, and >52 h/wk) or shift work was investigated using survey-weighted logistic regression analysis. ResultsLong commuting time (>120 min/d) combined with >52 working hours/week (OR: 7.88, 95% CI: 2.51-24.71) or combined with 41-52 h/wk (OR: 3.64, 95% CI: 2.15-6.14) was associated with a higher risk of insomnia symptoms compared with the reference group (working hours: 35-40 h/wk; daily commuting time: ≤60 minutes), after controlling for sex, age, socioeconomic factors, and work-related factors. Among shift workers, those with daily commuting time ≤60 minutes (OR: 1.71, 95% CI: 1.39-2.09), 61-120 minutes (OR: 2.63, 95% CI: 1.21-5.74), and >120 minutes (OR: 5.16, 95% CI: 2.14-12.44) had higher odds of insomnia symptoms than nonshift workers with ≤60 minutes daily commuting time. ConclusionLong working hours and shift work are associated with greater risk of insomnia symptoms.

Proposal of a Transport Planning Model for the Removal of Quarry Stone Using a Simulation

This article describes a transport planning model which applies a simulation to support decision-making in quarry operations. The analysis of the transport system was used as input for creating the model and the subsequent research. There are five loading points in the quarry from which, according to the plan, the quarry stone is transported to the crusher, where it proceeds for further processing. The required daily capacity of the downstream technological line is 3800 t/day. Based on the analysis, it was found that it was impossible to fulfill this requirement from the two loading points which were the furthest from the unloading point. For this reason, two simulation models of the transport system were created. The first simulation model is a transport system (loading, removal and dumping of quarry stone) from one loading point. Simulation experiments were performed on this model for all loading points. The findings from the analysis confirmed the results of the simulation experiments. Subsequently, a model of the combined quarry stone removal from two loading points was proposed. The model was designed for two variants of dividing the work shift into two work sections. In the model, which had a tabular form, the combinations of loading points and values of the minimum number of unloaded cars for stone removal were suitable to ensure its necessary daily capacity. The last part of this research was expanding the original model with an additional loading point. Several experiments were performed on this model. The aim of these experiments was to verify the combinations of loading points presented in the proposed model and the combinations of unsatisfactory loading points. Based on the research results, the transport planning model and simulation models are suitable additional tools for the decision-making process in removing quarry stone.

A modeling framework for evaluating proactive and reactive nurse rostering strategies — A case study from a Neonatal Intensive Care Unit

We develop a modeling framework for rostering, absence and demand uncertainty realization, and rerostering to perform detailed quantitative analyses of the robustness of nurse rosters. The framework reflects a real-life problem observed at the Department of Neonatal Intensive Care (DNIC) at St. Olavs Hospital in Trondheim, Norway, but is general and has a high transfer value with respect to using it to analyze roster robustness at other departments. We present multiple proactive strategies to enhance the stability of a roster and a reactive rerostering problem used to improve the flexibility. An extensive case study is performed using historical data from the department. The results show that there is a great potential to improve the stability and flexibility of the rosters using the best combination of strategies. We show that allowing nurses to trade extra weekend work for extra days off, assign surplus work hours evenly over all work shifts, and consider the absence profile of nurses when making the rosters are key strategies to create robust rosters.

An Ergonomic Risk Assessment System Based on 3D Human Pose Estimation and Collaborative Robot

Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning (ML) techniques for image processing to enable automated ergonomic risk assessment. In this context, this study aimed to develop a method of calculating joint angles from digital snapshots or videos using computer vision and ML techniques to achieve a more accurate evaluation of ergonomic risk. Starting with an ergonomic analysis, this study explored the use of a semi-supervised training method to detect the skeletons of workers and to estimate the positions and angles of their joints. A criticality index, based on RULA scores and fuzzy rules, is then calculated to evaluate possible corrective actions aimed at reducing WMSDs and improving production capacity using a collaborative robot that supports workers in carrying out critical operations. This method is tested in a real industrial case in which the manual assembly of electrical components is conducted, achieving a reduction in overall ergonomic stress of 13% and an increase in production capacity of 33% during a work shift. The proposed approach can overcome the limitations of recent developments based on computer vision or wearable sensors by performing an assessment with an objective and flexible approach to postural analysis development.

Working mode division and shift schedule extraction of a parallel hybrid power system

Abstract For hybrid electric vehicles, working mode division and shift schedule are two decisive factors affecting vehicle fuel economy. This paper introduces a method for vehicle working mode division and shift schedule determination on the basis of optimal system efficiency. The key index named ‘fuel-electricity conversion efficiency’ is defined and calculated. Via this method, unreasonable gear modes in specific working modes are excluded and the mode transition map and upshifting curves in each working mode are extracted. The simulation results demonstrate that, with the application of the single-motor multi-mode parallel hybrid power system, the proposed mode transition and shifting strategy can increase vehicle fuel economy by 12.99%.

Job Satisfaction and Associated Factors Among Primary Healthcare Workers: A Cross-Sectional Study From Qassim Region, Saudi Arabia.

Background The healthcare sector is one of the most important sectors in any country. Any disruption in the productivity of the workforce majorly affects healthcare entities. Job satisfaction directly interferes with the individual's productivity. Hence, the job satisfaction of healthcare workers (HCWs) is a fundamental issue to discuss, investigate, and study to improve the outputs to their maximal levels, especiallyif limited studies are done in this regard. Methodology A descriptive, cross-sectional, facility-based study was conducted among 302 HCWs in 30 primary healthcare centers (PHCCs) in the Qassim region, Saudi Arabia, using a pretested, validated, electronically self-administered "Satisfaction of Employees in Health Care" (SEHC) questionnaire. Our sample included physicians, nurses, pharmacists, dental workers, and lab and radiology technicians. Data were analyzed using SPSS version 29 (IBM Corp., Armonk, NY). Results The mean score of the overall perception of job satisfaction in primary healthcare staff was 3.9 (±1.01) out of one to five scale questions. This implies a high satisfaction in 71.2% (n = 215) of the sample. Nurses expressed the highest satisfaction with their roles, comprising 26.5% (n = 80)who reported high satisfaction. Second in line were physicians, demonstrating high satisfaction levels with 14.2% (n = 43). The third place was for lab technicians, 4.6% (n = 14) of whom expressed high satisfaction. Many factors were found to be significantly statistically associated with job satisfaction, including gender (p = 0.015), age (p = 0.001), job rank (p = 0.027), number of patients seen per day (p = 0.002), type of primary healthcare duty (p = 0.042), and health workers' years of experience (p = 0.000). Conclusion The study revealed that HCWsat primary healthcare facilities in the Qassim region, Saudi Arabia, were highly satisfied with their jobs. Significant statistical relationships were found between job satisfaction and factors such as years of experience, type of duty in PHCCs, and number of attendees per day. To improve HCWs' job satisfaction, we recommend that they receive additional organizational support and response to feedback, a reduced workload achieved through increased staffing, and flexible work shifts. We also recommend investigating satisfaction in the private healthcare sector.

The Effects of Work Shift and Work Environment on Employee Performance in the Printing Production Department at PT She Jong Suskes Abadi

This study aimed to determine the effects of working shifts and the working environment on employee performance. The population in this study included 50 employees of PT She Jong Sukses Abadi. This is causal associative research that employed the quantitative approach. Data were collected using a questionnaire. General analysis tests including the normality test, linearity test, multicollinearity test, beta-scedasticity test, and autocorrelation test were carried out in this research. The linear regression test consists of a simple linear, multiple linear regression, and determination tests. Hypothesis testing was done through t test and f test. The results of this research show that (1) work shift has a negative effect and significant effect on employee performance, (2) work environment has a positive and significant effect on employee performance, and (3) work shift and work environment simultaneously have effects on employee performance.

Breaking frames to create empathy: interaction between care practitioners and clients in homecare visits

Eldercare practitioners face pressures to make care work more efficient as the population ages and public resources become diminished in many countries. This study focuses on how empathic care is constructed during care practitioner–client interactions. The data are derived from observing seven care practitioners’ full work shifts in public homecare. With the help of positioning theory, our analysis identifies four positive deviations where breaking out of the medical and basic care frames opens up opportunities to form an empathic relationship and treat the client not only as a patient but also as a person on their life continuum.