Social work undergraduate curriculum and the readiness of the students to practice in the field of mental health
Bibliographic record
Abstract
With the growing economic and sociopolitical challenges, coupled with the COVID-19 pandemic and the increasing use of social media, Nigeria is recording a continuous increase in mental health problems. Social workers are expected to be at the forefront of mental health management, which begs the question of whether student social workers are ready for mental health practice. This paper sets out to find out the extent to which the content of the undergraduate curriculum of the Department of Social Work, University of Nigeria, Nsukka, prepares the students to practice in the field of mental health. In-depth interviews were used to collect data from 20 purposively selected undergraduate social work students. Thematic analysis was used to analyze the generated data. Findings show that the students who participated in the study believed that the curriculum has sufficient mental health courses to prepare them to practice in the field of mental health. Highlighting the paramount role of educators, the participants also indicated a need to improve the delivery of the course contents by educators. With this, it is necessary to introduce practical context-based and innovative delivery methods like the recent use of video simulations for mental health service delivery training.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".