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Related Concept Videos

Current Trends in Nursing II01:30

Current Trends in Nursing II

Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
Patient-centered Care01:13

Patient-centered Care

Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
Critical Thinking I01:24

Critical Thinking I

Critical thinking helps decision-making and allows nurses to recognize barriers to success and find solutions to possible issues. It helps to brainstorm and implement ideas to achieve goals. Critical thinking helps acknowledge and state workflow inefficiencies while improving management techniques. Nurses understand the value of critical thinking and look for fellow nurses with critical thinking skills to upgrade their professional standards. Critical thinking can advance a nurse's career with...
Ethical Issues01:27

Ethical Issues

Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
Ethical Dilemmas I01:17

Ethical Dilemmas I

Ethical dilemmas in nursing are of utmost importance, as they often arise from the tension between adhering to core ethical principles and the practical realities of healthcare delivery. These dilemmas require nurses to navigate complex situations where competing ethical considerations pull them in different directions.
Let us explore some examples to understand the potentially complex moral decisions nurses face.
Take the case of caring for minors, particularly in areas related to reproductive...
Critical Thinking II01:25

Critical Thinking II

Critical thinking is a cognitive process with several attributes. The attributes of critical thinking include the following:

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Related Experiment Videos

Artificial Intelligence and Mental Health Discourse: Methodological Reflections for Nursing Practice.

Chiung-Jung Wu1,2,3, Patrick C K Hung4,5

  • 1College of Health Sciences, Center of Innovations in Health Sciences, VinUniversity, Hanoi, Vietnam.

Issues in Mental Health Nursing
|June 18, 2026
PubMed
Summary

Artificial Intelligence (AI) shows promise for analyzing social media mental health data. While AI can identify patterns, its outputs are probabilistic and context-dependent, requiring careful ethical consideration and complementing clinical judgment.

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Area of Science:

  • Computational Social Science
  • Digital Mental Health
  • Artificial Intelligence in Healthcare

Background:

  • Social media provides extensive data on mental health discourse, distress, and support-seeking behaviors.
  • Artificial Intelligence (AI) methods can analyze this data to identify population-level patterns relevant to person-centered care.
  • Understanding these digital footprints can complement traditional clinical insights.

Purpose of the Study:

  • To evaluate AI-driven interaction-based classification and sentiment analysis of mental health discourse on social media.
  • To compare traditional machine learning, transformer models, and Large Language Models (LLMs) in analyzing Twitter data from users discussing mental health conditions.
  • To assess the utility and limitations of AI in understanding mental health communities online.

Main Methods:

  • Analysis of Twitter data from self-disclosing users across various mental health conditions.
  • Application of traditional machine learning, transformer models, and LLMs for classification and sentiment analysis.
  • Examination of interaction features and sentiment patterns within the discourse.

Main Results:

  • Interaction features moderately distinguished mental health accounts but lacked specificity across conditions.
  • Sentiment analysis revealed broadly consistent patterns across models, with mostly neutral discourse and heightened negative sentiment in specific diagnostic groups.
  • Large Language Models (LLMs) exhibited sensitivity to prompting and occasional output inconsistencies, raising concerns for reproducibility and interpretability.

Conclusions:

  • AI approaches offer potential for analyzing social media mental health data but are probabilistic and context-dependent.
  • These AI tools should augment, not substitute, clinical judgment in mental health care.
  • Ensuring transparent methods, addressing bias, and implementing ethical oversight are crucial for equitable and person-centered AI applications in mental health.