Faculty and Student Perceptions of Social Media in Social Work Education
Bibliographic record
Abstract
Today’s society is consumed with the digital age and related technologies. In the midst of this lies social networking. A review of the literature indicates that research is lacking to inform social work practice, and even less exists to guide institutions of higher education in ethical use of internet technology. An online survey of social work students and faculty was conducted to examine the perceptions of social media use in social work education. The population studied consisted of students (bachelors, masters and Ph.D. level) who were studying in accredited social work programs in the United States and Canada and faculty members teaching in accredited social work programs in the United States and Canada. The present analysis included 257 responses from individuals born before 1980 (143 faculty, 114 students) and 242 responses from those born in 1980 or later (7 faculty, 235 students). Preliminary Quantitative Results indicated that respondents born in 1980 or after were significantly more likely to use social media than those born in 1979 or earlier (96% and 87%, respectively; p<.001). Those born before 1980 were more than twice as likely to report having a positive experience with faculty or students as a result of social media (24.3%) than were those born in 1980 or later (9.8%; chi-square=16.396, p<.001). A small but significant proportion (9.3%) of respondents reported experiencing problems or concerns using social media with faculty or students, but there were no differences between groups. Only 6.6% of respondents indicated that their institution or department had a formal written policy about the use of social media as it relates to student/faculty relationships. Qualitative results and implications will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".