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Providing public healthcare to people is a major challenge for governments. In this sector, public-funded systems are grossly inadequate in India, and excessive commercialization and exploitation by the private sector are a stark reality. The cooperative healthcare model is emerging as an alternate system in Kerala with its strong service objective to challenge the woes of private healthcare. The cooperative hospitals in the state worked round the clock to serve the poor and needy during the devastating COVID-19 pandemic. The pandemic has also badly exposed the weakness of our healthcare system in the wake of challenges posed by an increase in demand for health services, especially in rural areas. The resultant rise in the cost of treatment has put severe strains on the people at a time when even their day to day jobs were in peril. India has a strong cooperative movement and world-class institutions to serve as models in each sector. The Thrikkakara Municipal Co-operative Hospital, located at Cochin in the Kerala State of India on which this case study is written was established by the Hospital Society Ltd. in 1999, as a project under the People’s Planning Programme of the Government. Today, it has grown into a medium-sized healthcare establishment with the prime objective ‘Modern healthcare to all at affordable costs’ and cater to an average of 700 outpatients a day. This case study reveals the inception, development over years, facilities available, operations, management, public interface, and outlook for the hospital to become a modern healthcare institution to serve the people still better.

This paper aims to automatically obtain the implant parameter from the CBCT images to improve the outcome of implant planning.

This paper proposes automatic simulated dental implant positioning on CBCT images, which can significantly improve the efficiency of implant planning. The authors introduce the fusion point calculation method for the missing tooth's long axis and root axis based on the dental arch line used to obtain the optimal fusion position. In addition, the authors proposed a semi-interactive visualization method of implant parameters that be automatically simulated by the authors' method. If the plan does not meet the doctor's requirements, the final implant plan can be fine-tuned to achieve the optimal effect.

A series of experimental results show that the method proposed in this paper greatly improves the feasibility and accuracy of the implant planning scheme, and the visualization method of planting parameters improves the planning efficiency and the friendliness of system use.

The proposed method can be applied to dental implant planning software to improve the communication efficiency between doctors, patients and technicians.

Globally, health problems are very common among prisoners. A mental state examination aims to help in recognising psychiatric problems among offenders and the possible association of these psychiatric issues with their committed crime. The legal-medical term “reduced criminal responsibility” refers to a weakened sense of reality and the ability to control one’s behaviour because of compromised mental health and without an evaluated need for forensic psychiatric hospitalisation. However, little is known about the actual need for the health care of prisoners with reduced criminal responsibility (PRCR). The purpose of this study was to explore treatment-related visits to prison by PRCR in Finland.

The research data comprise information on PRCR’s treatment-related visits and that of a matched control group (n = 222). Descriptive cross-tabulation with X²- and nonparametric Mann–Whitney U-tests and Cox regression analyses are applied.

The results show that almost every PRCR had at least one treatment-related visit during their sentences. Visits to a psychiatric hospital for prisoners, to the prison hospital and especially to a civil hospital are more common among PRCR. The need for treatment appears significantly earlier in their sentences.

These findings demonstrate the PRCR’s greater need for access to health services and the need for further development between the Health Care Services for Prisoners, Prison and Probation Service of Finland and public health and social services in Finland. More exploration of the medical reasons and locational distribution of the vast amount of civil hospitalisation is needed.

Drawing on the social presence theory, this study aims to explore how supplier–customer social media interactions influence supplier observers’ trust in the customers and what mechanisms contribute to variation in trust experience.

A total of 36 semi-structured interviews were conducted with Chinese suppliers using WeChat for business-to-business interactions. Data were analyzed in three steps: open coding, axial coding and selective coding.

Findings reveal that varied trust is based not only on the categories of social presence of interaction – whether social presence is embedded in informative interactions – but also on the perceived selectivity in social presence. Observer suppliers who experience selectivity during social and affective interactions create a perception of hidden information and an unhealthy relationship atmosphere, and report a sense of emotional vulnerability, thus eroding cognitive and affective trust.

The findings contribute new understandings to social presence theory by exploring the social presence of interactions in a supplier–supplier–customer triad and offer valuable insights into business-to-business social media literature by adopting a suppliers’ viewpoint to unpack the mechanisms of how social presence of interaction positively and negatively influences suppliers’ trust and behavioral responses.

Human evolution has witnessed the highest level of metamorphism overages. COVID-19 alarmed us when we were unceasingly running toward monetary benefits and money, the significance of health. That initiated the thought process of improvising the health and healthcare infrastructure, leading to the birth of the health cooperative as a reform. During the state of COVID services, operations of the hospital were unreachable due to the unavailability of doctors, facilities, hiked charges, and lack of insurance coverage made people disbelieve in the system. Many social activists propose the idea of healthcare cooperatives to foster healthcare needs. The study guides us to understand the roles of healthcare cooperatives like the establishment of service facilities, modernisation of existing facilities, expansion to various topographical locations, and healthcare education to the general public, repair, and renovate the instrumentation in the medical field. The study also finds the ways and means of self-sustainability of health cooperatives with dependency on government financial support during the initial take-off. The benefits of cooperatives contributing to the NDHM and supporting the development of healthcare infrastructure in rural areas. The study enables us to find the factors that healthcare cooperatives need to consider for providing the right benefit to the citizens and factors for the self-sustained and self-resilient mode of seamless operation. The study has two different data collecting instruments, one to collect the data from the public and other from healthcare professionals. The result of the study reveals the mechanisms through which healthcare cooperatives can provide well-structured healthcare support to the nation.

Until now, it is not clear whether there are differences in patient perception between multi-bedded rooms with two and four beds. The purpose of this study was to investigate the effect of the physical (i.e. room type) and psychosocial (i.e. kindness of roommates and extraversion) aspects on the patients’ experience (i.e. pleasantness of the room, anxiety, sleep quality) in multi-bedded rooms in an oncology ward.

A group of 84 hospitalized oncology patients completed a questionnaire on the day of departure. Room types were categorized into two groups: two-person and four-person rooms.

Multivariate logistic regression analyses with the minimum Akaike Information Criterion (AIC) showed no direct main effects of room type (two vs. four-person room), kindness of roommates and extraversion on pleasantness of the room, anxiety and sleep quality. However, the authors found an interaction effect between room type and extraversion on pleasantness of the room. Patients who score relatively high in extraversion rated the room as more pleasant when they stayed in a four-person rather than a two-person room. For patients relatively low in extraversion, room type was not related to pleasantness of the room.

The findings allow hospitals to better understand individual differences in patient experiences. Hospitals should inform patients about the benefits of the different room types and potential influences of personality (extraversion) so patients are empowered and can benefit from autonomy and the most appropriate place.

This study emphasizes the importance of including four-person rooms in an oncology ward, while new hospital facility layouts mainly include single-bed rooms.

The study aims to describe the impact of supervisor’s and co-worker’s ostracism on the employee's responses (emotional, psychological and behavioral) through the mediation of employees' efficacy needs and relational needs. Moreover, psychological capital is treated as a moderator to handle the adverse responses of ostracized employees.

The study employed a three-wave quantitative research design to gather data from employees and their respective supervisors who belonged to various healthcare units (N = 510) using self-administered close-ended questionnaires. After that, SmartPLS software was used to analyze the data through a structured equation modeling (SEM) technique.

The empirical results of the study endorsed that ostracism adversely (negatively) affects employees' responses (comprised of emotional, behavioral and psychological). Moreover, the results revealed that employees' needs (efficacy and relational) mediate the relationship between ostracism experienced by employees (supervisor’ and co-workers’ ostracism) and their emotional, behavioral and psychological responses. In addition, it is also evidenced that employees' psychological capital improves the negative association between employees' needs and responses.

The literature in this domain is scarce, and the theoretical stance is weak due to the traditional approaches that are more concerned with the outcomes rather than analyzing the employee's conditional what they are going through. The present study enhances the knowledge of the transactional mode of coping and its application to ostracism in the workplace. The results of the current study may also support the practitioners in formulating interventions to foster a favorable workplace environment.

This paper aims to improve our understanding of performance measurement systems in the health and care sector, by focussing on employee reactions to core performance measurement practices. Targets and monitoring are hypothesised to be associated with employee perceptions of job control, supportive management and job demands, which in turn, are expected to be linked to employee-wellbeing and organisational commitment.

Matched employee workplace data are extracted from a nationally representative and publicly available survey. Structural equation models are estimated.

Performance measurement systems are neither perceived as resources nor additional demands. Setting many targets and a focus on productivity can lead to negative employee outcomes, since these positively correlate with perceptions of job demands, which negatively correlate with employee wellbeing. However, monitoring financial performance and monitoring employee performance may be helpful to managers, as these are positively associated with employee perceptions of job control and supportive management, which positively correlate with job satisfaction and organisational commitment and, negatively, with anxiety. Overall, common criticisms of performance measurement systems in healthcare are questioned.

Given the lack of consensus on how performance measurement systems can influence employee experiences and outcomes, this study combines theories that argue for performance measurement systems in managing operations with models developed by psychologists to describe how perceptions of the work conditions can affect employee attitude and wellbeing. A conceptual model is therefore developed and tested, and potential direct and indirect effects of performance measurement systems in the health sector are inferred.

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.