A Bibliometric Analysis on No-Show Research: Status, Hotspots, Trends and Outlook
Bibliographic record
Abstract
No-show is a thorny issue within the social scope. It not only affects the sustainability of service system operation but also causes heavy irretrievable losses. To maintain and develop the sustainability of service, this paper adopts bibliometric technology to reflect the current status and future prospects about no-show research. And we strive to explore and summarize appointment scheduling methods for no-show problems. The bibliometric analysis was carried out from various aspects including research areas, countries/regions, institutions, journals, authors and author keywords based on papers harvested from Web of Science Core Collection database. The total 1197 papers show that the United States is in a leading position in this field, followed by England and Canada. University of London is the most productive institution with the highest total citations and H-Index. BMC Health Services Research ranks first as the most productive journal, followed by European Journal of Operational Research and Production and Operations Management. Through the analysis of hot articles, we can conclude that how to reduce the impact of no-shows on the sustainability of service systems has become the main research direction. In addition to appointment scheduling, other effective methods are also mentioned. Further study on these methods will be a major research direction 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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.120 | 0.221 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".