The relationship between Facebook behaviour and e‐professionalism: A questionnaire‐based cross‐sectional study among Greek dental students
Bibliographic record
Abstract
INTRODUCTION: The social media attitude of health science students might affect patients' opinion about the health profession and have negative impact on e-professionalism. The aim of this study is to investigate the behaviour of Greek dental students on Facebook, focusing on potentially unprofessional posts and the online student-patient relationship. MATERIALS AND METHODS: Five hundred and twelve dental students in Greece answered an anonymous, 23-item questionnaire including multiple-choice questions about various topics, including Facebook profile settings and content shared by dental students, student-patient relationship via Facebook; and students' perception about the impact of their online behaviour. RESULTS: 93.2% of responders had a Facebook profile and 80.5% admitted that their online attitude might affect patients' opinion about dental profession. However, 71.7% posted pictures from holidays, 41.5% from nightclubs, and 26.2% photographs wearing swimwear/underwear, while 12.8% expressed online political party predilection. One quarter of students in clinical years were Facebook friends with patients and 58% and 30% of them had online discussion about topics related or not to dentistry, respectively, while 6.8% of dental students had posted defamatory comments about the dental school, faculty members or academic staff on Facebook. DISCUSSION: In accordance with studies in other countries, most Greek dental students had a Facebook profile and, although the majority realised the impact of Facebook behaviour on e-professionalism, a considerable percentage posted unprofessional content. CONCLUSION: Dental students might fall into pitfalls when it comes to e-professionalism. As social media are becoming an integral part of life, there is need to include e-professionalism in dental education curriculum.
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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 | 0.001 | 0.002 |
| 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".