Reversing the Antibiotic Resistance “Yelp Effect” Through the Use of Emotionally Framed Responses to Negative Reviews of Providers: Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The overuse of antibiotics has rapidly made antimicrobial resistance a global public health challenge. There is an emerging trend where providers who perceive that their patients expect antibiotics are more likely to prescribe antibiotics unprompted or upon request. Particularly, health care providers have expressed concern that dissatisfied patients will provide disparaging online reviews, therefore threatening the reputation of the practice. To better deal with the negative reviews and inform patients, some health care staff directly respond to patients' online feedback. Engaging with patients' online reviews gives providers an opportunity to prevent reputational damage and improve patients' understanding of the antibiotic resistance problem. OBJECTIVE: We aim to test the effectiveness of different response strategies to the negative patient online reviews on the readers' perceptions of the health care provider and their perceptions related to antibiotics resistance. METHODS: Two experiments were conducted to examine the impact of message tactics (apologizing, inducing fear or guilt) that can be employed by health care providers when responding to patients' negative online feedback related to not receiving an antibiotic. RESULTS: Overall, our results demonstrated positive impacts of responding to patients' online reviews. In study 1, we found apologetic messaging and use of emotional appeals in the response were effective in making readers feel more favorable toward the message. Readers also expressed a greater credibility perception toward the provider and willingness to visit the clinic when emotional appeals were used. Findings from study 2 largely supported the effectiveness of a fear-based response in improving the readers' credibility perceptions and willingness to visit the clinic. The fear-inducing information was particularly effective among parent readers. CONCLUSIONS: This paper demonstrated that a strategic response to online patient complaints could prevent reputational damage and minimize the potential negative impacts of the review. The results also glean insight into the step toward developing a novel intervention-crafting a persuasive response to patients' negative feedback that can help improve the understanding of antibiotic resistance problems.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".