An Examination of Service Learning and Self-Efficacy for Masters Students Engaging in Substance Use Education
Bibliographic record
Abstract
This study explored perceptions of social work students before and after a service-learning project in which they worked with clients with substance abuse issues. Two areas were explored: (1) social work students’ perceptions of treating clients with substance use before and after the course “Addiction Treatment in Social Work” and the required service-learning project component; and (2) social work students’ self-efficacy before and after the addiction, service-learning project. Data-collection occurred through a pre-post self-efficacy survey, a questionnaire about interests in working with clients struggling with addiction and a course assignment. Students also completed a demographic questionnaire. Data were analyzed using Dedoose for the qualitative data component and SPSS for the quantitative components. Overall, findings from the quantitative and qualitative analyses were very positive. Although there were no significant increases in self-efficacy from pre-post-test the average scores did increase nearly 3.5 points. Students also indicated they were more willing to work with both individuals and groups/families dealing with addiction issues. Moreover, students reported an increase in insight, skills, community engagement and meaningful experiences even though they reported having feelings of doubt initially. Based on the findings, specialized training and service-learning opportunities in addictions for social work students is beneficial. Training should target appropriate skills, the distinct needs of people who are suffering from substance abuse disorders, and self-reflection regarding perceptions of substance use disorders.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".