The Significance of Physician-Patient Communication on Telemedicine Patients’ Health Outcomes: Evidence from Indonesia
Bibliographic record
Abstract
The importance of physician-patient communication on patient health outcomes has been globally known. Poor communication in clinical settings, including in telemedicine visits, has been identified as a key barrier to successful medical consultation. This barrier is even more prevalent among people from linguistically and culturally diverse communities. This study investigated the influence of physician-patient communication on telemedicine patient health outcomes in Indonesia, a developing country with great linguistic and cultural diversity. This study utilized secondary data from a telemedicine utilization survey conducted during the coronavirus disease 2019 (COVID-19) pandemic. Socioeconomic factors and communication features were included as predictors of patients' health improvement. Logistic regressions were utilized to examine the significance of the communication features on patients' health. The analysis results indicated that five communication features including the adequacy of consultation length, a timely physician response, the provision of an explanation of the medication and possible side effects, the patient's ability to utter their physical condition and opinion regarding medication goals, and the patient's ability to comprehend physician explanations and instructions were significantly associated with the patient's health outcomes. Physicians and healthcare providers should focus on the provision of communication features revealed in this study to elevate the likelihood of improved health conditions in telemedicine patients.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".