DEVELOPING SKILLS FOR DEVELOPMENTAL DISABILITIES: PRE-CLINICAL ELECTIVE EXPERIENCE DEMONSTRATES REPRODUCIBLE RESULTS
Bibliographic record
Abstract
BACKGROUND: Many medical students feel they are not trained adequately on caring for persons with developmental disabilities (PWDD). Additionally, there is a lack of literature about effective teaching methods (Troller et al. 2016; Salvador-Carulla et al., 2015). Students may have poor clinical skills when assessing developmental delays and experience discomfort when interacting with these patients and their families. Consequently, PWDD may not receive timely, empathetic care from their future clinicians (Sahin & Akyol, 2010). We previously developed and evaluated a pre-clinical 12-hour elective, “Developing Skills for Developmental Disabilities” (DSDD). Its primary learning objective was enhancing students’ knowledge and attitudes toward PWDD, with the goal of improving future patient-physician encounters. OBJECTIVES: We here look at reproducibility of learning outcomes over two years of offering this elective. DESIGN/METHODS: DSDD is a 12-hour pre-clinical elective developed collaboratively by a group of medical students and a developmental pediatrician. It provides training in disability, behaviour, and available interventions via faculty presentations, peer teaching, and observing a clinical intervention program for children with developmental disabilities. Students also complete written assignments and a personal narrative to self-assess feelings and beliefs. All students completed a 10-question survey (responses on a 5 point Likert scale) at the start and end of the elective, whereby they self-assessed confidence in skills and knowledge related to interacting with PWDD. Scores pre- and post-elective were compared using t-test analysis. RESULTS: Over 2 academic years, 44 students enrolled in the elective, and 41 (93%) completed it. Of the 41 participants, 19 (46%) had work experience with PWDD, and 26 (63%) had personal experience (family or friend related). Analysis of all participating students showed statistically significant (p<0.05) increases across all 10 self-reported scores. Comparison of the two cohorts of students separately revealed cohort 1 (n=20) showed statistically significant increases in 8 out of 10 scores, while cohort 2 (n=21) showed significant increases across all 10 scores. CONCLUSION: Overall, DSSD increased students’ self-reported confidence regarding PWDD. Additionally, data was consistent over two years, demonstrating reproducibility of the educational intervention. Future plans include adding a control group to address potential self-selection bias. Aspects of this approach may be adapted into teaching tools for office practice (e.g. assessing development, discussions with parents) when medical students are working alongside general pediatricians. If curricular interventions such as DSDD can help increase future physicians’ skills, PWDD may receive better care.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".