Information Technology and Social Work—The Dark Side or Light Side?
Bibliographic record
Abstract
The transition from industrial society to information society has had a significant impact upon social work. Benefits emerging have included simplified recording and assessment, electronic advocacy, interactive distance education opportunities and online group work and supervision. However, information technology can also be socially isolating and has led to new social issues including the creation of a false sense of safety, particularly among children and adolescents. Other concerns include the increased pace of work, the role of e-counselling and the emergence of a technologically inspired generation gap between new and established workers. Three focus groups, comprised of new BSW candidates, experienced part-time MSW candidates and field practice educators, were held to explore these issues. Themes generated included concerns regarding confidentiality, workload, and the compromising of basic social work practice and the therapeutic relationship. However, technology was also seen as having the potential to support geographically isolated clients and those with disabilities as well as providing another mechanism to connect with adolescents. Technology is ideology and while its advance is inevitable, social workers need to maintain a healthy scepticism while avoiding both unhealthy enthusiasm and unnecessary resistance, as technology will continue to create both challenges and opportunities for the profession.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".