Developing Skills for Developmental Disabilities: Assessing Efficacy in An Innovative Preclinical Elective
Bibliographic record
Abstract
Abstract BACKGROUND: Many medical students feel they are not trained with adequate skills and knowledge regarding cognitive and physical disability. Because of this, students may have poor clinical skills in assessing developmental delays and discomfort when interacting with these patients. “Developing Skills for Developmental Disabilities (DSDD)” is a 12-hour preclinical (years 1 and 2) elective developed collaboratively by a group of medical students and developmental pedi-atric faculty. This elective provides training in disability, behaviour, family challenges, and available interventions via faculty presentations, peer teaching, simulation sessions, and observation of a clinical developmental intervention program. OBJECTIVES: Our objective is to assess the efficacy of this new elective-which aims to assist students in correctly and comfortably identifying pediatric patients with, or at risk for, developmental delays and ininterac-tions withfamily members. DESIGN/METHODS: Participating students completed a 10-question survey (responses administered on a 5 point Likert scale) at the start and end of the elective, whereby they self-assessed confidence and knowledge when engagingchildren with developmental disabilities. This questionnaire was adapted from confidence surveys used for other curriculum assessments. RESULTS: 22 preclinical students enrolled in DSDD. Of these, 20 completed the elective and both surveys. Out ofthe 20 participants, 8 (40%) had previous work experience with disability, and 11 (55%) had personal experience (family or friend related). A significant (p0.001, CI 95%) increase in self-reported confidence was seen in 8 of10 survey questions. CONCLUSION: Overall, DSSD increased preclinical students' self-reported confidence and knowledge regarding pediatric patients with developmental disabilities. Future plans include arepeatsurveycomparing students who opt to take the elective with a control group, randomly selected from the remainder of the class. This willaddress any potential self-selection bias. Because this brief form of intervention was significant in increasing student confidence and skill, aspects of this approach may be adapted into teaching tools for office practice (e.g. assessing development and/ordiscussing care with parents) when medical students are working alongside general pediatricians seeing children with chronic developmental disabilities.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".