The Scientific Production of Providing Health Services to the Elderly: A Scientometric Study
Bibliographic record
Abstract
Introduction: Today, paying due attention to providing primary health services for the elderly is of high importance, and significant studies have been conducted in this regard in the world. The present study aimed to analyze the performance and drawing a scientific map of publications in the field of geriatric health services with a scientometric approach. Methods: In this scientometric study, all the scientific outputs in the field of geriatric health services in the Science Citation Index Expanded (SCIE-Web of Science) in the period from 1980 to August 18, 2023 were included. For data analysis, biblioshiny and Excel software were used. Results: Among 33238 documents in the field of geriatric health services indexed in the SCIE, the most of them were original articles. The growth trend of these documents was increasing so that the lowest number is related to 1987 with two documents and the highest number is related to 2022 with 3315 documents. BMJ Open was the most productive journal and Lancet was the most cited journal in this field. Univ Toronto was the most prolific institution and Cohen AD was the most prolific author. The keywords health services research and epidemiology were the most frequent. Conclusion: By presenting a comprehensive picture of the current state of studies in the field of geriatric health services, this study can determine the direction of future research. In addition, the findings of the present study can guide the direction and effectiveness of the research in this field.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.063 | 0.106 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".