PRIORITY SETTING IN AN ACUTE CARE HOSPITAL IN ARGENTINA: A QUALITATIVE CASE STUDY
Bibliographic record
Abstract
Purpose: To describe and evaluate priority setting in an Acute Care hospital in Argentina, using Accountability for Reasonableness, an ethical framework for fair priority setting.Methods: Case Study involving key informant interviews and document review.Thirty respondents were identified using a snowball sampling strategy.A modified thematic approach was used in analyzing the data.Results: Priorities are primarily determined at the Department of Health.The committee which is supposed to set priorities within the hospital was thought not to have much influence.Decisions were based on government policies and objectives, personal relationships, economic, political, historical and arbitrary reasons.Decisions at the DOH were publicized through internet; however, apart from the tenders and a general budget, details of hospital decisions were not publicized.CATA provided an accessible but ineffective forum for appeals.There were no clear mechanisms for appeals and leadership to ensure adherence to a fair process.Conclusions: In spite of their efforts to ensure fairness, Priority setting in the study hospital did not meet all the four conditions of a fair process.Policy discussions on improving legitimacy and fairness provided an opportunity for improving fairness in the hospital and Accountability for Reasonableness might be a useful framework for analysis and for identifying and improving strategies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".