Developing Equitable Policies for Task-Shifting from Supervisors to Resident Doctors in Indonesian Teaching Hospitals: A Legal and Ethical Framework
Bibliographic record
Abstract
Background: Task-shifting from supervisors to resident doctors is critical to specialist medical education in Indonesia. While broadly regulated under Law No. 17 of 2023, the absence of specific derivative regulations creates significant challenges, including role ambiguity, increased risk of medical errors, and inequities in supervision. Aims: This study aims to critically evaluate the proposed implementation of international best practices within Indonesia’s unique healthcare system and formulate a legal and ethical framework for equitable task delegation. Methods: Employing an empirical juridical approach, the study integrates in-depth interviews, document analysis, and legal doctrinal reviews across multiple teaching hospitals. Result: Findings indicate that the absence of derivative regulations leads to ambiguity in authority, inconsistent supervision practices, and heightened medical risks. International models such as the ACGME and EU directives offer useful insights but require contextual adaptation. This study further highlights systemic barriers in Indonesia, including resource constraints, logistical challenges, and political inertia. The critical role of supervisors is emphasised, necessitating clear training, certification, and accountability standards. Conclusion: There is an urgent need for a structured and localised task-shifting policy framework that integrates global standards while addressing Indonesia's practical realities. The proposed model outlines legal clarity, equitable delegation mechanisms, competency-based assignments, and institutional accountability.
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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 | 0.065 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| 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".