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The purpose of this study is to fill the research gap that exists with respect to availability of research on correctional facilities in India, which restricts one’s ability to comprehend the prison environment and recidivism from the perspective of the inmates. By examining inmates’ perception of their environment within correctional facilities, researchers and policymakers can identify specific aspects of the prison climate that may contribute to increased risks of reoffending. This understanding would not only help to improve prison conditions and encourage humane treatment, but it is also essential to support the inmates in their efforts to find and retain employment, achieve self-sufficiency and personal growth. It is also necessary to encourage the inmates to participate in a variety of therapeutic and prison programmes both inside and outside of prison to help them reintegrate into the community.

The present research investigated the association between negative perceptions of prison climate and the heightened probability of recidivism among a sample of 37 adult women convicts incarcerated in Tihar Prisons, Delhi. Data were obtained by administering CSS-M Hindi version and PCQ Hindi version to the convicts in person in the prison premises. Furthermore, the collected data were subjected to Pearson’s product moment correlation coefficient to analyse the correlation between the probability of recidivism and the dimensions of prison climate.

Results suggested that negative perceptions of relationships in prison (staff–inmate relationship, inmate–inmate relationship), safety, facilities (cell conditions, food quality, etc.), meaningful activities (recreation, education, yard time, etc.) and autonomy in prison were inversely associated with a higher probability of recidivism. The results from the current study demonstrated that recognising and taking cognisance of the inmates’ perceptions of prison environment on their well-being and their probability of recidivism is crucial for the successful implementation of interventions and policies within correctional systems.

The sample size procured for the research was relatively small, as the number of available women convicts in Tihar Prison at the time of the research was limited, making it difficult to generalise the findings. In addition, this investigation included solely women convicts, while undertrials and detenues were excluded, and thus does not represent all the individuals in conflict with the law.

The findings of the current investigation allow for the formulation of potential policy recommendations for improving the prison climate, which in turn might consequently impact future criminality.

Policymakers may use these findings when developing policies that foster a rehabilitative prison environment, as it can help in reducing the incidence of recidivism.

This study is unprecedented in its approach, especially with respect to the Indian context, as it sheds light on the relatively understudied association between the variables of perceived prison climate and the probability of recidivism among women convicts in India. The findings of this study add to the current body of research indicating that subjective experience and perception of prison climate have a substantial influence on the likelihood of recidivism.

Technology-enabled healthcare focuses on providing better information flow and coordination in healthcare operations. Technology-enabled health services enable hospitals to manage their resources effectively, maintain continuous patient engagement and provide seamless services without compromising their perceived quality.

This study investigates the role of technology-enabled health services in improving perceived healthcare quality among patients. Data are collected from the users (n = 418) of health platforms offered in multi-specialty hospitals. Multiple learners are employed to accurately represent the users' perceived quality regarding the perceived usefulness of the features provided via these digital health platforms.

The best-fitted model using a decision tree classifier (accuracy = 0.86) derives the accurate significance of features offered in the digital health platform in fostering perceived healthcare quality. Diet and lifestyle recommendations (30%) and chatting with health professionals (11%) are the top features offered in digital health platforms that primarily influence the perceived quality of healthcare among users.

The predictability of perceived quality with the individual features existing in the digital health platform, the significance of the features on the perceived healthcare quality and the prediction rules showing the combined effect of features on healthcare quality can help healthcare managers accelerate digital transformation in hospitals by improving their digital health platform, designing and offering new health packages while strengthening their e-infrastructure.

The study represents perceived healthcare quality with the features offered in digital health platforms using machine learners based on users' post-pandemic experience. By advancing digital platforms with more patient-centric features using emerging technologies, this model can further foresee its impact on the perceived quality of healthcare, offering valuable directions to healthcare service providers. The study is limited to focusing on digital health platforms that can deal with people's general healthcare needs.