The public’s attitude towards doctors’ use of Twitter and perceived professionalism: an exploratory study
Bibliographic record
Abstract
INTRODUCTION: Medical professionals use social media to interact with other healthcare professionals, discuss medical issues and promote healthcare information. These platforms have tremendous power to promote healthcare messages but also have potential to damage the profession if used inappropriately. It is currently unknown how others perceive medical doctors' Twitter activity and, therefore, we conducted an online survey exploring these views. METHODS: We used a Google Forms questionnaire consisting of 21 questions, which we distributed on Twitter, exploring doctors', patients', the public's and other healthcare professionals' views of doctors' Twitter activities. We investigated factors that were associated with mistrust by univariate and multivariate analysis. RESULTS: Seven-hundred and twenty-six respondents completed the survey. By univariate analysis, a higher proportion of non-doctors reported witnessing unprofessional behaviour and potential breaches of patient confidentiality compared with doctors (p<0.01). In addition, a significantly higher proportion of non-doctors felt that doctors' Twitter accounts should be monitored by both their employer and regulator when compared with doctors. By multivariate analysis, the main predictor of mistrust in the profession were those that had previously witnessed unprofessional behaviour (odds ratio 2.70; 95% confidence interval 2.08-3.33; p<0.01). CONCLUSION: There are discrepancies in how doctors and non-doctors view Twitter activity and significant mistrust in the profession was brought about by doctors' Twitter activity. To help limit this, adherence to current guidelines set out by the General Medical Council and British Medical Association is vital and doctors should be cautious about how their Twitter activity is professionally perceived by others before posting.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".