“Effective” Requesting
Bibliographic record
Abstract
BACKGROUND: Families are often asked to consent to the donation of their deceased relative's organs or tissues. These end-of-life conversations are important because they affect consent rates as well as the psychological impact of the decision for families. This scoping review of the literature on requesting family consent was prepared to support of the development of leading practice recommendations for end-of-life conversations with families of potential donors. A scoping review maps research activity in a field across a range of commentary and empirical study designs but does not attempt meta-synthesis of empirical data or quality assessment. METHODS: We performed a scoping review of the peer-reviewed literature from January 2000 to February 2015 on the questions of what constitutes an "effective" request and the factors that affect consent rates and family satisfaction with their decision and the process. This review includes a final set of 168 articles addressing (a) whether, when, and how to ask families for consent to donation or (b) characteristics of families or decedents that affect families' decisions or family satisfaction with their decisions. RESULTS: Six main themes were identified: (1) the objectives of requesting and encouraging family consent to donation, (2) the effect of the donation decision on family well-being, (3) the process of requesting family consent, (4) the impact of the quality of the care for the deceased and for the family, (5) the content and manner of the request for family consent, and (6) the characteristics of the family and deceased that affect the request for family consent. CONCLUSION: This scoping review found that there is a large literature on how to modify the process and manner of the request to increase family consent rates. Another important line of inquiry focuses on the psychological impact of the decision on the family. Although a scoping review does not attempt to synthesize results or draw evidence-based conclusions, the literature generally supports the intuitive expectations that compassionate and respectful care for the deceased and family, listening for and addressing family concerns, and an attitude to donation that is positive (but not solely procurement-focused) and is best for both consent rates and family well-being. Although the presumption is often that the primary objective of asking for family consent is to secure consent and donation, some ethical commentary on requesting consent emphasizes that this objective must be balanced with the parallel obligation to protect the psychological well-being of families. This places some constraints on the approaches used in family consent discussions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".