Dominikus David Biondi Situmorang, Rina Nurhudi Ramdhani, Pepi Nuroniah, Ahman
Background: Recent discussion on Ethical Big Data for Personalised Mental Health Nursing, highlights the pressing requirement to weave data ethics into mental health practice in relation to P4 medicine and systems thinking. Yet the bridge from ethical obligations to measurable methods is largely missing. The enhancement of statistical literacy is susceptible to culture shocks, which will then promote the development route of data-driven innovation toward humanistic and equitable mental health care. Objective: The purpose of this paper is to articulate ethical statistical paths that could implement big data in mental health practice. It aims to show how sound statistical methods may make compatible the principles of predictive, preventive, personalised, and participatory as well as the aims of (P4) medicine. Content: To complement Yıldız's ethical framework, this letter proposes statistical guidance influenced by Sutton's Applied Statistics Concepts for Counselors. The recommendations are organised around three major approaches: a regression-based predictive modelling approach to expose the predictive power of risk factors in a more transparent and fair manner; multivariate and multilevel analyses to maintain a focused approach to care while recognising that individual-level IRT might not exact the same influence at the system level; and non-parametric and robust statistics to sustain the desire for inclusiveness in terms of both population and individual data. The utilisation of these three sets of suggestions reveals how ethical and methodological rigour can co-occur in mental health data science. Implications: If the ethical underpinnings of this statistical education were integrated into nursing and counselling training, we might develop a data-literate crop of future practitioners. System-level interventions—for example, the creation of Ethical Big-Data Guidelines for Mental Health are called for to require statistical validation of predictive technologies before clinical use. Those kinds of rules would make big data an augmentation, rather than a replacement, for human judgment in mental health care. Conclusion: Responsible big data practice calls for a combination of ethical watchfulness and methodological care. The moment mental health professionals have the statistics and ethics of data explained to them, they turn information into wisdom—imagining a future that's both deeply scientific and deeply human. © 2025 John Wiley & Sons Ltd.
Department of Guidance and Counseling, Faculty of Education, Universitas Pendidikan Indonesia, Bandung, Indonesia
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