What Palliative Care Volunteers Would Like to Know About the Patients They Are Being Asked to Support
Bibliographic record
Abstract
A study was conducted to determine the kind of information palliative care volunteers would like to know about the patients they are being asked to support before they actually meet with them for the first time. Thirty-one palliative care volunteers responded to a brief questionnaire, developed for this study. At least half of the volunteers indicated that their coordinator provided them with the following patient information: (1) the patient's support system/family circumstances (eg, if there is any family), (2) the patient's diagnosis/disease, (3) the patient's age, and (4) the patient's location (address/room number). Overall, the volunteers were very satisfied with the information their coordinators passed along to them. Volunteers rated medical information (eg, the patient's diagnosis) and relationship information (eg, the patient's marital status) as being more important to them than personal information (eg, the patient's interests and hobbies). The 3 most important sources of patient information, mentioned by at least half of the volunteers, were (1) their coordinator, (2) the patient's family members, and (3) the patient himself or herself. Only a few volunteers described issues around confidentiality that had arisen in their work (eg, being a volunteer in a small town, where people know what you are doing).
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".