title: Identifying and predicting subgroups of information needs among cancer patients: an initial study using latent class analysis.
abstract: OBJECTIVE
  Understanding how the information needs of cancer patients (CaPts) vary is important because met information needs affect health outcomes and CaPts' satisfaction. The goals of the study were to identify subgroups of CaPts based on self-reported cancer- and treatment-related information needs and to determine whether subgroups could be predicted on the basis of selected sociodemographic, clinical and clinician-patient relationship variables.
  
  
  METHODS
  Three hundred twenty-three CaPts participated in a survey using the "Cancer Patients Information Needs" scale, which is a new tool for measuring cancer-related information needs. The number of information need subgroups and need profiles within each subgroup was identified using latent class analysis (LCA). Multinomial logistic regression was applied to predict class membership.
  
  
  RESULTS
  LCA identified a model of five subgroups exhibiting differences in type and extent of CaPts' unmet information needs: a subgroup with "no unmet needs" (31.4% of the sample), two subgroups with "high level of psychosocial unmet information needs" (27.0% and 12.0%), a subgroup with "high level of purely medical unmet information needs" (16.0%) and a subgroup with "high level of medical and psychosocial unmet information needs" (13.6%). An assessment of sociodemographic and clinical characteristics revealed that younger CaPts and CaPts' requiring psychological support seem to belong to subgroups with a higher level of unmet information needs. However, the most significant predictor for the subgroups with unmet information needs is a good clinician-patient relationship, i.e. subjective perception of high level of trust in and caring attention from nurses together with high degree of physician empathy seems to be predictive for inclusion in the subgroup with no unmet information needs.
  
  
  CONCLUSIONS
  The results of our study can be used by oncology nurses and physicians to increase their awareness of the complexity and heterogeneity of information needs among CaPts and of clinically significant subgroups of CaPts. Moreover, regression analyses indicate the following association: Nurses and physicians seem to be able to reduce CaPts' unmet information needs by establishing a relationship with the patient, which is trusting, caring and empathic.
authors.affiliation: Gerhard Kienle Institute for Medical Theory, Integrative and Anthroposophic Medicine, Medical Department, Private University of Witten/Herdecke, Alfred-Herrhausenstr. 50, 58448, Witten, Germany. melanie.neumann@uni-wh.de
authors.forename: Melanie
authors.initials: M
authors.lastname: Neumann
authors.forename: Markus
authors.initials: M
authors.lastname: Wirtz
authors.forename: Nicole
authors.initials: N
authors.lastname: Ernstmann
authors.forename: Oliver
authors.initials: O
authors.lastname: Ommen
authors.forename: Alfred
authors.initials: A
authors.lastname: Längler
authors.forename: Friedrich
authors.initials: F
authors.lastname: Edelhäuser
authors.forename: Christian
authors.initials: C
authors.lastname: Scheffer
authors.forename: Diethard
authors.initials: D
authors.lastname: Tauschel
authors.forename: Holger
authors.initials: H
authors.lastname: Pfaff
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003430:Cross-Sectional Studies; D005260:Female; D007722:Health Knowledge, Attitudes, Practice; D006301:Health Services Needs and Demand; D006306:Health Surveys; D006801:Humans; D033181:Information Dissemination; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D020380:Needs Assessment; D009369:Neoplasms; D016017:Odds Ratio; D010353:Patient Education as Topic / Q000379:methods*; D017060:Patient Satisfaction; D010817:Physician-Patient Relations; D012189:Retrospective Studies; D013223:Statistics as Topic; D013997:Time Factors; D055815:Young Adult