Finding common ground to achieve a “good death”: family physicians working with substitute decision-makers of dying patients. A qualitative grounded theory study
Bibliographic record
Abstract
BACKGROUND: Substitute decision-makers are integral to the care of dying patients and make many healthcare decisions for patients. Unfortunately, conflict between physicians and surrogate decision-makers is not uncommon in end-of-life care and this could contribute to a "bad death" experience for the patient and family. We aim to describe Canadian family physicians' experiences of conflict with substitute decision-makers of dying patients to identify factors that may facilitate or hinder the end-of-life decision-making process. This insight will help determine how to best manage these complex situations, ultimately improving the overall care of dying patients. METHODS: Grounded Theory methodology was used with semi-structured interviews of family physicians in Edmonton, Canada, who experienced conflict with substitute decision-makers of dying patients. Purposeful sampling included maximum variation and theoretical sampling strategies. Interviews were audio-taped, and transcribed verbatim. Transcripts, field notes and memos were coded using the constant-comparative method to identify key concepts until saturation was achieved and a theoretical framework emerged. RESULTS: Eleven family physicians with a range of 3 to 40 years in clinical practice participated.The family physicians expressed a desire to achieve a "good death" and described their role in positively influencing the experience of death.Finding Common Ground to Achieve a "Good Death" for the Patient emerged as an important process which includes 1) Building Mutual Trust and Rapport through identifying key players and delivering manageable amounts of information, 2) Understanding One Another through active listening and ultimately, and 3) Making Informed, Shared Decisions. Facilitators and barriers to achieving Common Ground were identified. Barriers were linked to conflict. The inability to resolve an overt conflict may lead to an impasse at any point. A process for Resolving an Impasse is described. CONCLUSIONS: A novel framework for developing Common Ground to manage conflicts during end-of-life decision-making discussions may assist in achieving a "good death". These results could aid in educating physicians, learners, and the public on how to achieve productive collaborative relationships during end-of-life decision-making for dying patients, and ultimately improve their deaths.
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 | 0.028 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".