Experience of Violence of Social Work Students in Field Studies: A Critical Review
Bibliographic record
Abstract
Social workers work with clients who are distressed, have unmet needs, or have complicated psychosocial issues (substance abuse, homelessness, a health-care crisis, intimate partner violence, etc.). The purpose of this critical analysis is to critically evaluate what is known about social work students' experiences with violence in field education. This critical review synthesizes the existing literature on the experiences of violent social work students during their fieldwork. The study's objectives are to: 1) what sorts of violence do social work students encounter during field education? 2) What training and education are required to respond to violence experienced by social work students in field education? To achieve the goals of this review, we critically examined seven relevant works through a postmodern feminism lens. We identified three major themes: social work as a gendered profession, diverse types of violence in field education settings, and a lack of violence prevention and safety training. A critical study, such as participatory action research with social work students in field education, as well as community-based research with field educators and supervisors, is advised. Furthermore, social work students must receive occupational health and safety training so that they can prepare for and respond to violence in their field studies. This study verifies social work's classification as a gendered 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".