Ethical decision making during a healthcare crisis: a resource allocation framework and tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic has strained healthcare resources the world over, requiring healthcare providers to make resource allocation decisions under extraordinary pressures. A year later, our understanding of COVID-19 has advanced, but our process for making ethical decisions surrounding resource allocation has not. During the first wave of the pandemic, our institution uniformly ramped-down clinical activity to accommodate the anticipated demands of COVID-19, resulting in resource waste and inefficiency. In preparation for the second wave, we sought to make such ramp down decisions more prudently and ethically. We report the development of a tool that can be used to make fair and ethical decisions in times of resource scarcity. We formed an interprofessional team to develop and use this tool to ensure that a diverse range of stakeholder perspectives were represented in this development process. This team, called the clinical activity recovery team, established institutional objectives that were combined with well-established procedural values, substantive ethical principles and decision-making criteria by using a variation on the well-known accountability for reasonableness ethical framework. The result of this is a stepwise, semiquantitative, ethical decision tool that can be applied to resource allocation challenges in order to reach fair and ethically defensible decisions. This ethical decision tool can be applied in various contexts and may prove useful at both the institutional and the departmental level; indeed this is how it is applied at our centre. As the second wave of COVID-19 strains healthcare resources, this tool can help clinical leaders to make fair decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.013 |
| 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 it