To Friend or Not to Friend? Social Networking and Faculty Perceptions of Online Professionalism
Bibliographic record
Abstract
PURPOSE: To assess faculty perceptions of professional boundaries and trainee-posted content on social networking sites (SNS). METHOD: In June 2010, the Clerkship Directors in Internal Medicine conducted its annual survey of U.S. and Canadian member institutions. The survey included sections on demographics and social networking. The authors used descriptive statistics and tests of association to analyze the Likert scale responses and qualitatively analyzed the free-text responses. RESULTS: Of 110 institutional members, 82 (75%) responded to the survey. Of the 40 respondents who reported current or past SNS use, 21 (53%) reported receiving a "friend request" from a current student and 25 (63%) from a current resident. Of these, 4 (19%) accepted the student request and 12 (48%) accepted the resident request. Sixty-three of 80 (79%) felt it was inappropriate to send a friend request to a current student, 61 (76%) to accept a current student's request, 42 (53%) to become friends with a current resident, and 61 (81%) to become friends with a current patient. Becoming friends with a former student, former resident, or colleague was perceived as more appropriate. Younger respondents were less likely to deem specific student behaviors inappropriate (odds ratio [OR] 0.18-0.79; adjusted OR 0.12-0.86, controlling for respondents' sex, rank, and SNS use), although none reached statistical significance. CONCLUSIONS: Some internal medicine educators are using SNSs and interacting with trainees online. Their perceptions on the appropriateness of social networking behaviors provide some consensus for professional boundaries between faculty and trainees in the digital world.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".