Contribution of short-term global clinical health experience to the leadership competency of health professionals: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: Globalisation has increased the opportunities for health professionals working in developed countries to provide clinical and educational support in developing countries. However, how these experiences contribute to the leadership competency of health professionals is unclear; therefore, this study explored this with the objective of analysing the process of developing individual leadership competency. DESIGN: This is a qualitative descriptive study. Qualitative descriptive study is widely used in healthcare research, particularly to describe the nature of various healthcare phenomena. Qualitative descriptive data were collected in face-to-face, semistructured interviews. SETTING: The authors interviewed Japanese health professionals who participated in an international medical cooperation project as part of a multinational medical team between July 2017 and March 2018, and analysed and interpreted the data using a social constructivism paradigm. PARTICIPANTS: The authors interviewed 20 research participants, including 5 nurses, 5 dentists and 10 physicians with an average of 15.3 years of clinical experience. RESULTS: The interviews identified 58 emergent themes related to their leadership competency, 23 of which affected the actual medical care in their own institutions. The authors categorised the 58 emergent themes into seven competency areas: leadership concepts, teambuilding, direction setting, communication, business skills, working with others and self-development. The authors identified the relationships among each competency and identified differences between professions: nurses particularly reflected on their empathic attitudes towards patient after global clinical health experience; dentists tended to reflect on their business skills; physicians tended to reflect on their leadership concepts and teambuilding. CONCLUSIONS: This study clarified the leadership competency gained through short-term global clinical health experience and the process of individual leadership competency development. The findings provide expected learning competency for those considering medical practice in developing or other countries in the future.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".