Bedside Rationing by Health Practitioners: A Case Study in a Ugandan Hospital
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe bedside rationing by health practitioners in a teaching hospital in Uganda. METHODS: This was a case study involving in-depth interviews. A modified thematic approach was used in data analysis. Types of decisions, the decision-making process, key players, and hospital-, medical-, and patient-related considerations in the process were identified. Klein's 6 forms of rationing were used to identify the forms of rationing used. The setting was a tertiary hospital in Uganda. Theoretical sampling was used to identify 40 doctors and 16 nurses from the Departments of Medicine, Surgery, Paediatrics, and Obstetric and Gynaecology. RESULTS: Four types of bedside rationing decisions were identified: 1) which patients are seen first, 2) which treatment the patients receive, 3) which patients are admitted, and 4) which patients are taken to the operating theatre first. Hospital-related considerations regarding bedside rationing included the hospital budget and number of beds; medical-related considerations included the patient's diagnosis and effectiveness of treatment; and patient-related considerations included poverty, social status, and age. All forms of rationing (denial, dilution, deflection, deterrence, delay, and termination) were practiced. CONCLUSION: Although bedside rationing decisions in the study hospital seem somewhat similar to that in developed countries, the rationing of 1st-line drugs by health practitioners in Uganda is complex, difficult, and different from what has been described in industrialized countries. The complexity and severity of the consequences of the bedside decisions necessitate the development of resource-sensitive clinical guidelines and transparent decision-making processes to foster patients' understanding of the reasons and the procedures and to ensure fair decision-making processes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".