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Record W2804046282 · doi:10.1093/pch/pxy054.085

DEVELOPING SKILLS FOR DEVELOPMENTAL DISABILITIES PRE-CLINICAL ELECTIVE: DIFFERENT SETTING AND POPULATION PRODUCES CONSISTENT IMPROVEMENT IN STUDENT CONFIDENCE

2018· article· en· W2804046282 on OpenAlexaff
Andy Le, Alexis Fong-Leboeuf, Debra Andrews

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsGlenrose Rehabilitation Hospital
Fundersnot available
KeywordsLikert scaleMedicinePopulationMedical educationScale (ratio)PsychologyFamily medicineDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Medical students feel they are inadequately trained in caring for patients with developmental disabilities (PWDD) (Troller et al. 2016; Salvador-Carulla et al., 2015). Consequently, PWDD may not receive timely, empathetic care from their future clinicians (Sahin and Akyol, 2010). We developed a preclinical elective, “Developing Skills with Developmental Disabilities” (DSDD), to improve student knowledge, skills, and attitudes toward paediatric PWDD. The first cohorts worked with pre-schoolers; DSDD was effective in improving student confidence working with PWDD (Penner et al. 2017). The current project compared the efficacy of DSDD using a hospital-based day-school for elementary-aged children, to previous cohorts. OBJECTIVES Our goal was to determine if changing the population being observed and the setting in which they are being observed could reproduce improvement in student confidence as seen in past cohorts. DESIGN/METHODS The DSDD module was an elective offered to preclinical medical students for credit. Students were given 6 hours of didactics on child development, assistive technologies, and breaking bad news. Students also participated in 6 clinical hours at the Glenrose Rehabilitation Hospital, where they observed school-aged PWDD in a classroom and interacted with an interdisciplinary team. Students also interviewed children’s families during medical intakes. Students completed pre- and post-elective surveys administered on a 5-point Likert scale. Questions pertained to students’ self-perceived comfort and knowledge regarding PWDD. Scores pre- and post-elective were compared using t-test analysis. This data was compared to data collected from previous cohorts, which used the same survey. RESULTS 24 students registered for DSDD, and 21 surveys were able to be analysed. Statistically significant (p<0.01) increases were present in 9/10 self-reported scores, with the statistically insignificant score pertaining to confidence using positive reinforcement. There was no significant difference in pre- and post-elective score improvement when comparing this cohort with past cohorts, across all scores. The critical components of DSDD were maintained across setting changes with significant (p<0.01) increases in students’ self-reported confidence and knowledge in working with PWDD. CONCLUSION This elective demonstrates effectiveness in different settings and ages. The general structure and principles of this elective may be applied by Paediatricians to improve medical education. Examples include having students attend developmental programmes they might provide support for, using a short set of parent interview questions and/or a child observation to improve developmental teaching, and allocating time for interaction with other allied health professionals to better understand their roles in the management of paediatric PWDD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.063
GPT teacher head0.458
Teacher spread0.395 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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