Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic presented many challenges for patients with palliative care needs and their care providers. During the early days of the pandemic, visitors were restricted on our palliative care units. These restrictions separated patients from their families and caregivers and led to considerable suffering for patients, families, and health-care providers. Using clinical vignettes that illustrate the suffering caused by visiting restrictions during the pandemic, the introduction of a new concept to help predict when health-care providers might be moved to advocate for their patients is introduced. METHODS: We report 3 cases of patients admitted to a palliative care unit during the COVID-19 pandemic and discuss the visiting restrictions placed on their families. In reviewing the cases, we coined a new concept, the "Suffering Quotient" (SQ), to help understand why clinical staff might be motivated to advocate for an exemption to the visiting restrictions in one situation and not another. RESULTS: This paper uses 3 cases to illustrate a new concept that we have coined the Suffering Quotient. The Suffering Quotient (SQ) = Perceived Individual (or small group) Suffering/Perceived Population Suffering. This paper also explores factors that influence perceived individual suffering (the numerator) and perceived population suffering (the denominator) from the perspective of the health-care provider. SIGNIFICANCE OF RESULTS: The SQ provides a means of weighing perceived patient and family suffering against perceived contextual population suffering. It reflects the threshold beyond which health-care providers, or other outside observers, are moved to advocate for the patient and ultimately how far they might be prepared to go. The SQ offers a potential means of predicting observer responses when they are exposed to multiple suffering scenarios, such as those that occurred during the COVID-19 pandemic.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".