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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Nurse retention discourse in YouTube comments: a structural topic modeling analysis.

Taewha Lee¹, JooHyun Lee¹

¹Mo-Im Kim Nursing Research Institute, College of Nursing, Yonsei University, Seoul, South Korea.

Journal of Korean Academy of Nursing

|June 2, 2026

Summary

This summary is machine-generated.

Public discourse on nurse retention highlights systemic issues, not individual choices. Analysis of YouTube comments reveals how events like COVID-19 and policy changes shape these discussions, informing future healthcare policy.

Keywords:

Natural language processing Nurses Social media Unsupervised machine learning Workforce

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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Published on: May 31, 2019

Area of Science:

Social sciences, public health, communication studies.
Analysis of online public discourse and healthcare workforce dynamics.

Background:

Nurse retention is a critical issue impacting healthcare systems globally.
Existing research often focuses on individual-level factors influencing nurses' decisions to leave the profession.
Understanding societal-level discourse is crucial for a comprehensive view of nurse retention challenges.

Purpose of the Study:

To analyze public discourse on nurse retention using YouTube comments from 2018-2024.
To examine how nurse retention issues are collectively framed in online public spheres.
To extend individual-level retention research to societal-level discourse analysis.

Main Methods:

Collected 32,399 comments from 226 YouTube videos related to nurse retention.
Employed structural topic modeling and topic correlation network analysis.
Utilized community detection to identify discourse coalitions and framing.

Main Results:

Identified 14 substantive topics, with 'International nursing migration and career pathways' and 'Disputes regarding the scope of practice' being most prevalent.
Observed discourse shifts linked to focusing events: COVID-19, the 2022 Nursing Law, and the 2024 medical school quota expansion.
Discovered two main discourse communities ('professional identity' and 'structural exploitation') bridged by 'international migration'.

Conclusions:

Public discourse frames nurse retention as a systemic and structural issue, not solely individual professional dissatisfaction.
Collective framing patterns complement traditional research and can inform policy design.
Identifying resonant frames and discourse fragmentation is key for effective policy implementation.