Online patient feedback: a cross-sectional survey of the attitudes and experiences of United Kingdom health care professionals
Bibliographic record
Abstract
Objectives Online patient feedback is a growing phenomenon but little is known about health professional attitudes and behaviours in relation to it. We aimed to identify the characteristics, attitudes and self-reported behaviours and experiences of doctors and nurses towards online feedback from their patients or their carers. Methods We conducted a cross-sectional self-completed online questionnaire of 1001 registered doctors and 749 nurses and midwives involved in direct patient care in the United Kingdom. Results Just over a quarter (27.7% or 277/1001) of doctors and 21% (157/749) of nurses were aware that patients/carers had provided online feedback about an episode of care in which they were involved, and 20.5% (205/1001) of doctors and 11.1% (83/749) of nurses had experienced online feedback about them as an individual practitioner. Feedback on reviews/ratings sites was seen as more useful than social media feedback to help improve services. Both types of feedback were more likely to be seen as useful by nurses compared with doctors and by hospital-based professionals compared with those based in community settings. Doctors were more likely than nurses to believe that online feedback is unrepresentative and generally negative in tone. The majority of respondents had never encouraged patients/carers to leave online feedback. Conclusions Despite enthusiasm from health policymakers, many health care professionals have little direct experience of online feedback, and rarely encourage it, as they view it as unrepresentative and with limited value for improving the quality of health services. The difference in opinion between doctors and nurses has the potential to disrupt any use of online patient feedback. The findings have implications for policy and practice in how online patient feedback is solicited and acted upon.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".