Privacy, professionalism and Facebook: a dilemma for young doctors
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the nature and extent of use of the social networking service Facebook by young medical graduates, and their utilisation of privacy options. METHODS: We carried out a cross-sectional survey of the use of Facebook by recent medical graduates, accessing material potentially available to a wider public. Data were then categorised and analysed. Survey subjects were 338 doctors who had graduated from the University of Otago in 2006 and 2007 and were registered with the Medical Council of New Zealand. Main outcome measures were Facebook membership, utilisation of privacy options, and the nature and extent of the material revealed. RESULTS: A total of 220 (65%) graduates had Facebook accounts; 138 (63%) of these had activated their privacy options, restricting their information to 'Friends'. Of the remaining 82 accounts that were more publicly available, 30 (37%) revealed users' sexual orientation, 13 (16%) revealed their religious views, 35 (43%) indicated their relationship status, 38 (46%) showed photographs of the users drinking alcohol, eight (10%) showed images of the users intoxicated and 37 (45%) showed photographs of the users engaged in healthy behaviours. A total of 54 (66%) members had used their accounts within the last week, indicating active use. CONCLUSIONS: Young doctors are active members of Facebook. A quarter of the doctors in our survey sample did not use the privacy options, rendering the information they revealed readily available to a wider public. This information, although it included some healthy behaviours, also revealed personal information that might cause distress to patients or alter the professional boundary between patient and practitioner, as well as information that could bring the profession into disrepute (e.g. belonging to groups like 'Perverts united'). Educators and regulators need to consider how best to advise students and doctors on societal changes in the concepts of what is public and what is private.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".