When to Say “Yes” and When to Say “No”
Bibliographic record
Abstract
A total of 79 hospice palliative care volunteers from 2 community-based hospice programs responded to a 27-item Boundary Issues Questionnaire that was specifically developed for this study. Volunteers were asked to indicate whether or not they considered each item (eg, "Lend personal belongings to a patient or family," "Agree to be a patient's power of attorney," "Attend/go into a patient's medical appointment") to be something they should not do and to indicate whether or not they have ever done it. On the basis of the volunteers' responses, the authors distinguished between "definite boundary issues" (things volunteers should never do, for example, "Accept money from a patient or family"), "potential boundary issues" (things volunteers should stop and think twice about doing, for example, "Accept a gift from a patient or family"), and "questionable boundary issues" (things volunteers should be aware of doing, for example, "Give your home phone number to a patient or family"). The implications of these findings for training volunteers are discussed and the need for clear and unambiguous organizational policies and procedures to preserve boundaries is stressed. Without clear policies, etc, community-based hospice programs may be putting themselves at legal risk.
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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 | 0.008 | 0.024 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".