Bibliographic record
Abstract
This study examines academic research trends and the change of patterns by analyzing researches related to hospital management registered in Korea Citation Index(KCI) from 2010 to 2014 and offers basic information for interests and future research demands in the field of hospital management As research subjects, a total of 694 published articles were selected. It``s to analyze them by dividing into research topics, methods and author.s characteristics, and to present them by classifying the period into 2010-2011, 2012-2013, 3rd quarter of 2014 since there was no significant difference in the result between adjacent years. As a result, Korean Journal of Hospital Management has accounted for the highest published rate year after year. In the research topic analysis, it showed a decreasing trend in these research topics as medical marketing and patient satisfaction which became the biggest issue in 2010-2011 significantly were lower in 2014, but an increasing trend in job satisfaction, job stress, labor administration and workforce productivity. The most frequently cited keywords were hospital employees, job satisfaction, organizational commitment, job stress, turnover intention. According to the research method analysis, the survey was the most popular method for data collection. However, Interview and medical records as data sources showed a decline trend. As analysis methods, multivariate analysis of quantitative methods was most used. Finally, as a result of analyzing main author.s characteristics, the ratio of the authors belonging to health administration and nursing departments of the academic world increased gradually. In the regional distribution, organizations in Seoul are most common, those in DaeguㆍGyeongbuk areas, foreign organizations showed a tendency to decrease. This is the first study to examine the annual trend on hospital management-related research issues among articles published in domestic journal and we found qualitative and quantitative advances in hospital management research filed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".