The Challenges of Social Work for Practical Major Students at Times of Crises from the Perspective of Mentors
Bibliographic record
Abstract
This paper examines the challenges, as reported from the professors’ perspective, faced by students in their field course of a baccalaureate social work program during the coronavirus pandemic. The paper also proposes improvements in the field training course for use in any future crisis. Furthermore, it aims to highlight the importance of field training for the students. Using a qualitative approach, researchers interviewed a sample of eight faculty mentors (3 men and 5 women) who taught and supervised students in the baccalaureate programs from the University of Jordan, Al-Balqa' Applied University, and Dhofar University. Study results show that students face numerous challenges, such as lack of communication between them and the supervisors in the field institutions, student dissatisfaction with the concept and implementation of online-training training, struggle in learning basic skills to handle certain intervention situations, and the difficulty of applying these skills remotely. Recommendation: Increased use of smart devices and digital platforms during field practice. The creation of new education materials that include visual elements that can be used more interactively in an online environment.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".