Factors Affecting Long-Term-Care Residents' Decision-Making Processes as They Formulate Advance Directives
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe factors contributing to the decision-making processes of elderly persons as they formulate advance directives in long-term care. DESIGN AND METHODS: This study was qualitative, based on grounded theory. Recruitment was purposive and continued until saturation was reached. Nine residents of a long-term-care facility were interviewed by use of a semistructured format. Open and axial coding of interview transcripts were carried out and the factors contributing to the decision process were defined. RESULTS: Elders based their decisions primarily on information gathered from personal experiences with death and illness. They obtained very little information from professionals or the media. Major factors considered by elders as they weighed information included spiritual, emotional, and social considerations. IMPLICATIONS: The factors considered during the decision-making process were oriented more toward the individual's experiences and less on contributions from objective sources than anticipated. Decision making for advance directives is a highly personalized process. The approach of health professionals when assisting with end-of-life decision making should be planned with these contributing factors in mind, so that the services offered to the individuals in this population best meet their needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".