Looking Back: Primary Decision Makers' Narratives about the Decision to Withdraw Life-Sustaining Treatment from a Family Member.
Bibliographic record
Abstract
Background. Advances in biomedical and critical care technology make it possible to prolong life in patients with severe illness or injury; however, these innovations render choices regarding Life-Sustaining Treatments (LST) more complex for the decision maker. This study addressed a gap in the literature regarding how primary decision makers reflected on the time they were responsible for making a critical decision to withdraw LST for their family member who experienced an unexpected illness or trauma. Purpose. The purpose of this study was to examine the primary decision makers’ narratives about the decision to withdraw Life Sustaining Treatment (LST) of a family member, at least six months or more after having made the decision. Method. An unconstrained deductive content analysis using a sensitizing frame of the Ottawa Decision Support Framework guided the data collection and analysis of interview data based on the pre-determined categories of decisional need, decision support, decision quality, and aftermath. A non-randomized sample of 9 primary decision makers responded to questions in a structured interview lasting 20 to 50 minutes. Questions focused on the participants’ thoughts and feelings during the time decisions were made for their family member. Findings. All participants were Caucasian females with an average age of 60. The relationship to the family member was either a spouse or adult child. At the time of the interview, the interval since the time of death of the family member varied from 9 months to 25 years. Thirteen themes within the pre-determined categories of the Ottawa Decision Support Framework emerged. Six themes emerged in decisional need, four in decision support, and three in decision quality categories. One of the overarching themes from this data analysis was the value of support from family and friends when participants made the decision to discontinue LST. Two themes not addressed within the framework were the importance of faith to the decision makers and that the decision was the hardest one they ever made. Themes regarding aftermath of the decision reflected the participants’ feelings about wishing they had shared information with the dying family member and knowing they made the right decision. Conclusions. When the primary decision makers face LST decisions, they have multiple needs and value support from their family and friends. In order to improve support to primary decision makers, health care providers should use components of the Ottawa Decision Support Framework, such as coaching, decision tools, or counseling. By becoming
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".