Predictive model for chaplain taxonomy patterns identified through latent class analysis among infants in a pediatric inpatient setting

Xiaobo Zhong, Madhu Mazumdar, Vansh Sharma, Lina Jandorf, Denise Welsh, Deborah B. Marin

Research output: Contribution to journalArticlepeer-review

Abstract

This study investigated patterns of spiritual care provided to inpatient infants and their parents, based on a taxonomy developed to describe spiritual care activities provided by chaplains. Data from 821 visits with 433 patients were included in the analyses. We applied a data-driven statistical approach, Latent Class Analysis (LCA), to identify patterns of taxonomy items that may be used for spiritual care. Three distinct patterns were identified and a predictive model was built to link a series of predictors to these patterns. Hospital length of stay and whether a visit is an initial or follow-up within an admission were significantly associated with the identified taxonomy patterns. These findings are helpful in understanding predictors and the nature of spiritual care delivery in an inpatient setting with infants. To our knowledge, this is the first application of LCA in research related to healthcare chaplaincy.

Original languageEnglish
Pages (from-to)118-128
Number of pages11
JournalJournal of Health Care Chaplaincy
Volume27
Issue number2
DOIs
StatePublished - 2021

Keywords

  • Taxonomy
  • chaplain
  • electronic health record (EHR)
  • infant
  • latent class analysis

Fingerprint

Dive into the research topics of 'Predictive model for chaplain taxonomy patterns identified through latent class analysis among infants in a pediatric inpatient setting'. Together they form a unique fingerprint.

Cite this