Assessing Rehabilitation Eligibility of Older Patients: An Ethical Analysis of the Impact of Bias
Bibliographic record
Abstract
Hospitalized older patients are more vulnerable to physical or cognitive functional decline. Inpatient rehabilitation programs improve significantly their functional status and may prevent their admission to nursing homes. While inpatient rehabilitation institutions have established admission criteria that can be seen as objective, the risk of bias remains and raises the question of equitable access for more vulnerable populations such as older patients. This paper reviews some established eligibility criteria for inpatient rehabilitation by examining a framework used in Montreal, Québec, Canada for assessing rehabilitation eligibility and by applying this framework to a case study. It also highlights the unique ethical challenges presented by the assessment of older patients. We conclude that in order to appropriately protect the vulnerable population of older patients in the context of priority setting and allocation of scarce resources, there is a need to establish more specific criteria that can better guide the assessment of this particular population.
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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 | 0.453 | 0.628 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".