For this study, we used data from the Palliative Care Communication Research Initiative (PCCRI), a multi-site cohort study of naturally occurring inpatient palliative care consultations(15, 16). The PCCRI was designed to understand the relation between clinical communication and patient-centered outcomes. The 6-month cohort data includes directly observed and audio-recorded palliative care consultations; patient/proxy and clinician self-report questionnaires both before and the day after consultation; post-consultation in-depth interviews; and medical/administrative records. The audio data for the PC consultations and follow-up interviews were converted to a transcription of text data for analysis.
The study data were collected for 231 hospitalized patients with advanced cancer who consulted with PC in two large academic medical centers in the United States. For our study we used the patient/proxy questionnaire for patients’ demographic information (age, gender, race, education, financial insecurity) and self-reported preference for comfort-directed care near EOL, and attitudinal variables such as distressing uncertainty, spiritual distress, emotional distress, religious affiliation (if any), and whether patients felt their spiritual needs were being met by their religious community or the medical system. We used verbatim transcriptions of the palliative care conversations to identify moral words using the MFD data dictionary described in the previous paragraph.
The psychologists who developed the MFD did this by classifying words in one of the five moral foundations, by vice or virtue. This results in 10 potential “dimensions” of moral words in the text: each of the 5 foundations with “vice” and “virtue” categories for each foundation.
We used 231 audio-recorded and transcribed inpatient PC consultations and data from baseline and follow-up patient questionnaires at two large academic medical centers in the United States. With these data, we identified different moral expressions using text mining techniques and natural language processing. The words that each patient or proxy said were combined into a single corpus of text. We included only text used by patients, not physicians or other members of the conversation. The corpus was then split into a list of individual words, which were set to lowercase and stemmed. Stop words, such as ”and”, ”the”, and ”of”, were removed from each corpus to reduce the noise of the data.
First, we added up all the morality words used by the patient in a PC consultation, and counted, after pre-processing, the total number of words used by the patient as a proxy for the length of the conversation. We then disaggregated the words from the data dictionary to create the 10 different categories of moral terminology in the PC consultations. We created a matrix for all categories where a word from the Moral Foundations Theory Dictionary (MFD) occurred in a patient’s text, that patient was assigned a value of ”1” for that word’s associated MFD category. The text mining process was performed with Python 3.7.3.
After merging the data from the text with the data from the PC survey, we analyzed the data in a few steps, adopting an exploratory approach to test relations between underlying factors and moral expressions in the PC consultations.
First, we used latent class analysis (LCA) to classify the patterns of MFD expressions into mutually exclusive classes. LCA is based on the idea that a discrete latent variable accounts for observed associations between a set of indicators, such that, conditional on the latent class variable, these associations become insignificant.(17) The ten indicators in our analysis were created after the text mining phase: each one indicated whether a patient used a vice- or virtue- related word in one of the five dimensions of the MFT. In addition to the indicators (which are used for the actual classification) covariates were included in the model to explain class membership: age, gender, race, education, financial security and religion. We also included self-reported variables regarding patient’s spiritual needs, whether they reported emotional, spiritual or uncertainty-related distress, and preferences for comfort-directed treatment at EOL and looked at patterns of several of the attitudinal variables. Our analyses focused on preferences for comfort-directed EOL treatment; emotional, spiritual or uncertainty-related distress; and whether patients felt their spiritual needs were being met by (1) their religious community or (2) the medical system. EOL preference was defined by the answer to a survey question: “During the last few months of my life, I would prefer a plan of treatment that focused on my comfort and quality of life, even if that meant not living quite as long”, which is answered by a 5-point Likert scale.
The questions related to emotional feelings, also answered by 5-point Likert scales, included:
- Over the past two days, how much have you been bothered by emotional problems such as feeling anxious, depressed, irritable, or downhearted and blue.
- Over the past two days, how much have you been bothered by uncertainty about what to expect from the course of your illness?
- Over the past two days, how much have you felt at peace?
Questions related to spiritual needs included: “How much are your spiritual needs being supported by a religious community (like clergy or members of a congregation)?”, and: “How much are your spiritual needs being supported by the medical system (doctors, nurses and chaplains)?” where both were answered by “completely-quite a bit-moderately-slightly-not at all”.
Second, to explore which factors were associated with patients’ use of vice- or virtue-related words, and their use of words belonging to the 5 different foundations of morality, we used Poisson regressions. Age was a continuous variable, race was represented by a binary variable for “white”, education was categorical, and “financial security” was represented by a categorical variable: “When you think about the amount of income that you have available in a typical month, how often is it enough for things you really need like food, clothing, medicine, repairs to the home, and transportation?” – answered by “all the time”, “most of the time”, “some of the time”. We included a binary variable for “Christian” religion and one for “other religion” which included Judaism, Islam, Hinduism, Buddhism, and “other” from the survey data. We also controlled for the total amount of words used by the patient in the consultations, as a proxy for the length of the conversation.