Self-reported comfort treating severe mental illnesses among pre-doctoral graduate students in clinical psychology
Bibliographic record
Abstract
BACKGROUND: One possible explanation for the dearth of psychologists working in severe mental illness (SMI) areas is a lack of training opportunities. Recent studies have shown that while training opportunities have increased, there remain fewer resources available for SMI training compared to other disorders. AIM: Examines whether students express discomfort working with this population and whether they are satisfied with their level of training in SMI. METHODS: One-hundred sixty-nine students currently enrolled in doctoral programs in clinical psychology in the United States and Canada were surveyed for their comfort treating and satisfaction with training related to a number of disorders. RESULTS: RESULTS indicate that students are significantly less comfortable treating and finding a referral for a patient with schizophrenia as well as dissatisfied with their current training in SMI and desirous of more training. Regression analyses showed that dissatisfaction with training predicted a desire for more training; however, discomfort in treating people with SMI did not predict a desire for more training in this sample. This pattern generally held across disorders. CONCLUSIONS: Our results suggest general discomfort among students surveyed in treating SMI compared to other 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".