The Doctor-Patient relationship: A bibliometric analysis
Bibliographic record
Abstract
Objective Doctor-patient communication is an essential clinical practice necessary to improve overall patient experience and their adherence to treatment. This form of communication involves first, listening without interruptions and then, conveying information in a clear and concise manner. Methods A bibliometric analysis was conducted on publications extracted from the Web of Science database related to doctor-patient communication from 2012 to 2022 using the VOSviewer 1.6.15 software to visualize trends. Results 20,376 articles were from 2012 to 2022 met the inclusion criteria of being recognized by the search phrase “physician-patient communication.” Throughout the defined time period, USA, Canada, and Germany consistently took the top three positions in terms of publishing the most articles regarding the topic. Additionally, the Patient Education and Counseling Journal was the journal with the most publications regarding the topic. Innovation A bibliometric analysis is a relatively novel way to frame research in a given area. It allows researchers to analyze trends in publication, and capture data from multiple disciplines. Conclusion The number of articles published annually regarding doctor-patient communication has constantly been increasing from 2012-2022, demonstrating its importance as a crucial component of effective health care.
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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 | 0.019 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.228 | 0.279 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".