Investigating physician leadership competencies in rural and remote areas of the province of Aceh, Indonesia
Bibliographic record
Abstract
BACKGROUNDS: Globally, the most rural healthcare systems are lagging behind those of urban healthcare systems. Especially in rural and remote areas, the essential resources to provide principal health services are inadequate. It is purported that physicians have an important role in healthcare systems. Unfortunately, there is a paucity of studies on physician leadership development in Asia, especially on how to enhance physician leadership competencies in rural and remote low-resource settings. This study aimed to investigate doctors' perceptions of existing and needed physician leadership competencies based on their experiences in primary care settings in low-resource rural and remote areas are in Indonesia. METHODS: We performed a qualitative study with a phenomenological approach. Eighteen primary care doctors, who worked in rural and remote areas of Aceh, Indonesia, purposively selected, were interviewed. Prior to the interview, participants were asked to select the top-five skills they deemed most essential for their work based on the five domains of the 'Lead Self', 'Engage Others', 'Achieve Results', 'Develop Coalitions' and 'Systems Transformation' (LEADS) framework. We then performed a thematic analysis of the interview transcripts. RESULTS: We identified the following qualities a good physician leader in low-resource rural and remote settings should possess: (1) cultural sensitivity skills; (2) a strong character that includes courage and determination; and (3) creativity and flexibility skills. CONCLUSIONS: Local cultural and infrastructural factors create a need for several different competencies within the LEADS framework. A profound amount of cultural sensitivity was considered the most important in addition to the ability to be resilient, versatile and ready for creative problem-solving.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".