Evolution of Telepsychiatry: Scientometric Analysis of Telepsychiatry Publications Between 1986 and 2019
Bibliographic record
Abstract
Introduction: Although telepsychiatry has a long history, medical literature lacks a scientometric study evaluating telepsychiatry publications. The purpose of this study was to perform a holistic analysis of telepsychiatry articles published between 1986 and 2019. Methods: We used the “telepsychiatry” keyword for our search and included all documents indexed in Web of Science (Clarivate Analytics, USA) Core Collection between 1986 and 2019, revealing a total of 1,020 articles, of which only 224 were open access. Results: The peak year for publication was 2015, with 96 articles. The United States ranked first with 601 documents followed by Australia and Canada. The University of California and University of Washington were the most productive institutions and, again, 8 of the 10 leading institutions were from the United States. The peak year for citations was 2019, with a total of 2,080 records. Discussion: We believe that systematic approaches are needed to reveal the positive and negative features of telepsychiatry practice, especially from countries where this method is widely utilized, to elucidate the need for telepsychiatry in other countries/regions and to determine how its use can be increased in regions with limited access to health care workers. Conclusion: Although scientific interest in telepsychiatry appears to have increased almost every year since 1986, it has been observed that this interest is still concentrated in certain countries, such as the United States, Australia, and Canada, indicating that telepsychiatry may not have gained use in other countries.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.103 | 0.123 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| 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".