Impact of adverse media reporting on public perceptions of the doctor–patient relationship in China: an analysis with propensity score matching method
Bibliographic record
Abstract
OBJECTIVES: Numerous studies indicate that the doctor-patient relationship in China is facing serious challenges. This study examined the impact of China Central Television's negative coverage of high medicines prices on both doctors' and patients' opinions of the doctor-patient relationship. SETTING: Data were collected in a national survey conducted during 19 December 2016 to 11 January 2017 which targeted 136 public tertiary hospitals across the country. PARTICIPANTS: All patients and doctors who submitted completed questionnaire were retrieved from the survey database. INTERVENTION: The study used propensity score matching method to match the respondents before and after China Central Television's news report about high medicines prices which was given at 00:00 hours on 24 December 2016. OUTCOME MEASURE: Perception scores were calculated based on the five-point Likert scales to measure the opinions of the doctor-patient relationship. RESULTS: The perception scores of the doctor-patient relationship were significantly affected by the negative media coverage for hospitalised patients, who scored 1.18 lower on the doctor-patient relationship following the report (p=0.006, 95% CI 0.34 to 2.02), and doctors who scored 5.96 points lower on the same scale (p<0.001, 95% CI 4.11 to 7.82). Score for the ambulatory patients was unaffected by exposure to the adverse news report (p=0.05). CONCLUSION: Chinese national media's reporting of adverse news negatively affected the perceptions of the doctor-patient relationship among both inpatients and doctors. A better understanding of the role of mass media in the formation of opinion and trust between doctors and patients may permit strategies for managing the media, in order to improve public perceptions of the doctor-patient relationship.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".