TAKING UP A SOCIAL WORK IDENTITY: PREPARING UNDERGRADUATE STUDENTS FOR ENTRY-LEVEL GENERALIST PRACTICE
Bibliographic record
Abstract
Undergraduate students who are new to the social work profession enter into a complex socialization experience. New to the profession, they are expected to absorb a variety of knowledge and skills learned in separate courses and then integrate these together into an authentic performance as a social worker. Additionally, students are trained to confront personal bias and must learn to socially locate themselves and begin to form their own critical framework for understanding and responding to the influence of oppressive discourses on everyday systems, structures, practices, and experiences. Although the socialization process is essential to the development of a social work identity, there is little description in the social work education literature about students’ experiences of that process. Through using a practice demonstration video as teaching-learning resource for an undergraduate child welfare practice course, this paper presents our effort to examine how students position themselves in relation to practices and skills shown in the social worker-service-user interactions on the video. For the purposes of this pilot study, we used qualitative content analysis to generate a descriptive analysis of students’ written responses to viewing the video interactions. Analyses of these responses revealed that, although students were able to name and discuss the practices they observed through the video demonstration, they were also challenged by the interactions that demonstrated conflict, client resistance, and the overt use of power in the worker-service-user relationship. The results demonstrate the need for greater understanding of how students become socialized into the profession and develop their own unique identities as social workers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".