The Impact of Strengths-Based Assessment Education on Undergraduate Students’ Knowledge of Disorders and Mental Illness Stigma
Bibliographic record
Abstract
Background: While abnormal psychology courses have traditionally focused on psychopathology, there are several benefits to adopting a strengths-based approach. Objective: This study examined the teaching of a strengths-based assessment approach (the DICE-PM Model), compared to teaching as usual, in an undergraduate abnormal psychology course. Method: Two sections of an abnormal psychology course were taught a strengths-based assessment approach while two sections were taught as usual. All participants completed measures of knowledge of psychological disorders and mental illness stigma at the beginning and end of the semester. Results: Both groups demonstrated significant improvements in knowledge of disorders and a significant decrease in mental illness stigma with the exception of one category assessed (recovery), generally with small effect sizes. Those in the strengths group, compared to the control, showed a significantly greater decrease in mental illness stigma involving anxiety related to others with mental illness, though also with a small effect. Conclusion: Findings suggest strengths-based assessment education does not compromise the instruction of psychological disorders and is equivalent to a traditional abnormal psychology course in reducing mental illness stigma. Teaching Implications: Such an approach may be beneficial early in students’ education to reduce mental illness stigma and promote comprehensive assessment practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".