The relation between resident-related factors and care problems in nursing homes: a multi-level analysis.

Background: Care problems such as decubitus and fall incidents are prevalent in nursing homes. Yet, research regarding explanatory factors on these care problems is scarce. The aim of this study is twofold: (1) to identify the degree to which a diverse set of resident-related factors (e.

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults.

Objectives: In long-term care for older adults, large amounts of text are collected relating to the quality of care, such as transcribed interviews. Researchers currently analyze textual data manually to gain insights, which is a time-consuming process. Text mining could provide a solution, as this methodology can be used to analyze large amounts of text automatically.

The development of an automatic speech recognition model using interview data from long-term care for older adults.

Objective: In long-term care (LTC) for older adults, interviews are used to collect client perspectives that are often recorded and transcribed verbatim, which is a time-consuming, tedious task. Automatic speech recognition (ASR) could provide a solution; however, current ASR systems are not effective for certain demographic groups. This study aims to show how data from specific groups, such as older adults or people with accents, can be used to develop an effective ASR.

Text mining in long-term care: Exploring the usefulness of artificial intelligence in a nursing home setting.

Objectives: In nursing homes, narrative data are collected to evaluate quality of care as perceived by residents or their family members. This results in a large amount of textual data. However, as the volume of data increases, it becomes beyond the capability of humans to analyze it.