Understanding Gender-Specific Daily Care Preferences: Topic Modeling Study

Abstract

Background: Daily preferences are a reflection of how adults wish to have their needs and values addressed, contributing to joy and satisfaction in their daily lives. Clinical settings often regard older adults as a uniform group, neglecting the diversity within this population, which results in a shortfall of person-centered care that overlooks their distinct daily care preferences. At the heart of person-centered care lies the imperative to comprehend and integrate these preferences into the care process. Recognizing and addressing gender differences in older adults is critical to customizing care plans, thereby optimizing quality of life and well-being for individuals. This study addresses the need to understand the diverse daily care preferences of adults, particularly among older populations, who represent a growing demographic with unique needs and interests.

Objective: This study aims to identify and analyze the key themes and daily care preferences from unstructured adult text narratives with a focus on uncovering gender-specific variations.

Methods: This study used 4350 deidentified, unstructured textual data from MyDirectives (MyDirectives, Inc), an interactive online platform. Advanced topic modeling techniques were used to extract meaningful themes, and gender-specific term frequency and distribution were examined to identify gender differences in these elements.

Results: The study sample included 2883 women (mean age 63.02, SD 13.69 years) and 1467 men (mean age 67.07, SD 11.73 years). Our analysis identified six major themes: (1) "entertainment" (12.14%, 528/4350), (2) "music" (10.39%, 452/4350), (3) "personal interests and memories" (38.18%, 1661/4350), (4) "intimate relationships" (14.92%, 649/4350), (5) "natural comforts" (16.18%, 704/4350), and (6) "emotional, cultural, and spiritual foundations" (8.18%, 356/4350). Gender differences were evident: women were more likely to express preferences for "personal interests and memories" (40.7% vs 33.3%), "natural comforts" (18.4% vs 11.9%), and "emotional and spiritual foundations" (9.3% vs 6.1%) than men. Men expressed stronger preferences for "entertainment" (18.1% vs 9.1%) and "music" (16.8% vs 7.2%). Common terms across all participants included "dog," "love," "friends," and "book." Notably, the study revealed significant gender differences in daily care preferences, especially regarding familial relationships and entertainment choices.

Conclusions: The findings underscore the importance of recognizing individual daily care preferences in person-centered care, particularly regarding gender. Understanding these preferences is crucial for improving care quality and patient satisfaction, thereby enhancing the overall quality of life for adults receiving care across our health care system.

Keywords: KNIME; care preference; data mining; gender differences; online platform; patient-centered care; quality of life; topic modeling.

Figure 1

Flowchart illustrating the data selection process for the topic modeling study on daily care preferences among US adults. The figure outlines participant inclusion criteria, exclusion steps, and the final sample (N=4350) used for natural language processing analysis.

Figure 2

The Konstanz Information Miner workflow of topic modeling applied to unstructured text responses collected via MyDirectives. The process includes tokenization, lemmatization, and removal of stopwords before topic modeling using latent Dirichlet allocation. Tokenization refers to the process of breaking down a stream of text into smaller units called tokens, typically words. Lemmatization is the process of converting variations of a word that reflect tense or number to its root form, called a lemma. For instance, the words “writes,” “wrote,” and “written” would all be lemmatized to “write.” Stopwords are words that are deemed to have little to no intrinsic value for understanding the essence of the content. These commonly include words such as “I,” “my,” “on,” “of,” “what,” “and,” “the,” and “from.” Model optimization was guided by perplexity, coherence, and the elbow method. LDA: latent Dirichlet allocation.

Figure 3

Determining the optimal number of topics for latent Dirichlet allocation modeling. (A) Coherence and perplexity scores across topic counts. (B) Elbow method illustrating model fit. Analyses were conducted using Python and Konstanz Information Miner.

Figure 4

Gender-based keywords and thematic differences in daily care preferences among US adults. (A) Heat map of normalized term frequencies for the top 40 most common words by gender. (B) Radar chart comparing the distribution of 6 major themes across women and men subgroups, based on topic probability weights.