Navigating the dual role of physician and clinician investigator in end-of-life research
Bibliographic record
Abstract
The challenges of recruiting participants for end-of-life (EOL) research are multifaceted. The Last Gift study at the University of California San Diego, an observational study for people with HIV (PWH) with terminal illness, appeals to the altruism of potential participants and community of allied health providers. Involvement of the latter group highlights a potential ethical conundrum of a "dual role", as primary care providers (PCPs) navigate between clinical responsibilities to their patients, along with opportunities to discuss clinical research. To explore this conundrum and better understand study recruitment dynamics of the Last Gift study, we analyzed screening and enrollment data for a 12-month period (2022-2023). We found that PCPs can play an important role in the recruitment of PWH into EOL research, as having PCPs discuss the study with potential participants yielded more successful enrollments than contact by the study team alone. Our manuscript proposes considerations to mitigate dual role conflicts, including ensuring ethical awareness, prioritizing clinical care and offering strategies to involve PCPs in recruitment without causing unnecessary burden or coercion. These insights aim to guide similar EOL research efforts, emphasizing the need for balanced, ethical recruitment processes in the sensitive context of terminal illness.
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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 | 0.461 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.023 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".