Personal Characteristics and Learning Preference in End-of-Life Decision Making of Chronically Ill Community Dwelling Elders
Bibliographic record
Abstract
Leigh Ann Bonney, RN, MSN; Susan Kossman, RN, PhD; MyoungJin Kim, PhD; Wendy Woith, PhD, Caroline Mallory, PhD Illinois State University, Mennonite College of Nursing Personal Characteristics and Learning Preference in End-of-Life Decision Making of Chronically Ill Community Dwelling Elders Purpose and Background: Clear decision-making (DM) about end-of-life [EOL] contributes to a good death This requires knowledge of life-sustaining treatment with lack of knowledge contributing to feelings of decisional conflict Decision aids can increase knowledge, but elders' preferred learning methods are unknown. Identifying characteristics associated with preferred learning method and decisional conflict can help nurses tailor information to assist in end-of-life (EOL) DM . The purpose of this study is to identify factors associated with EOL DM in chronically ill community-dwelling elders Conceptual framework: The Ottawa Decision Support Framework (O'Connor, 2006). Method: Exploratory, descriptive study using paper or online surveys with a convenience sample of community dwelling, chronically ill elders over age 75. Survey included: Population Needs Assessment, Newest Vital Sign assessment [health literacy], Symptom Distress Scale, and the 16-item Decisional Conflict Scale Research questions are: a) What patient characteristics are associated with decisional conflict about EOL DM? b) What factors are associated with preferences for decision aid to assist EOL care learning? c) What is the feasibility of using an online survey methodology? Results: N =115 [15 online, 100 paper surveys]. Participants were Caucasian, predominately female [68.7%] with a mean age of 81.6. Most felt they were adequately knowledgeable about EOL options [78.3%] and treatment [78.8%]. Decision support preferences were booklets/pamphlets [22.6%] and discussion with healthcare providers [58.3%]. Education beyond high school was significantly associated with lower decisional conflict [R2 = __ . p = .017]. High School or lower education was associated with preferences for booklet/pamphlet decision aid [p=.039]. No statistically significant associations among characteristics and preferences for discussion with healthcare providers. Fifteen surveys were completed online out of 74 instances of surveys opened. Conclusions: These findings mirror other studies of younger participants. Data suggests that chronically ill community-dwelling elders over age 75 prefer to learn about EOL by talking to their healthcare provider and/or from a booklet/pamphlet. Findings indicate that more education leads to less decisional conflict.. The feasibility of online surveys seems limited in this population. Keywords: End of life, decision making, decisional conflict Note: The research team consisted of my research chair - Dr. Susan Kossman and members of my committee: Dr. Caroline Mallory, Dr.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".