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Record W3056321063 · doi:10.1093/pch/pxaa068.053

54 Consistency in a Pediatric Developmental Disability Elective for Preclinical Medical Students: Five Years of Student Experience

2020· article· en· W3056321063 on OpenAlexaff
Irina Simin, Siobhan Thornton, Alexis Fong-Leboeuf, Debra Andrews

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCurriculumLikert scaleMedicineMedical educationConsistency (knowledge bases)Session (web analytics)PsychologyFamily medicineDevelopmental psychologyPedagogy

Abstract

fetched live from OpenAlex

Abstract Background There is an increasing recognition for medical students to receive more training in caring for patients with developmental disabilities (PWDD). Studies have found that providing training and encounter opportunities with PWDD for medical students improves their attitudes, comfort level, and knowledge. PWDD can have more sensitivity to changes in health than others, highlighting the importance of providing educational opportunities for medical professionals. To mitigate these outcomes, this preclinical 12-hour elective: “Developing Skills for Developmental Disabilities” (DSDD) was developed with the primary learning objective of improving students’ knowledge of and attitudes toward developmental disabilities in pediatrics. Objectives The current study’s objective was to evaluate the consistency of students’ perceived confidence ratings in assessing and managing children presenting with developmental delay or disability, despite changes in workplace educational setting across 5 years. Design/Methods Students received 6 hours of content-specific didactic teachings in addition to the standard second year developmental pediatrics curriculum. Content was provided by a team of developmental pediatricians and physiatrists. Didactic session topics included child development, estimating developmental age, assistive technologies, and breaking bad news, to supplement the 6 hours of clinical experience at a rehabilitation hospital. Students attended medical assessments with the opportunity to conduct a brief interview with the child’s family, observe pediatric PWDD in treatment programs, and interact with interdisciplinary teams. Students were given pre- and post-elective self-assessment surveys administered on a 5-point Likert scale. Questions pertained to students’ self-perceived comfort and knowledge regarding pediatric PWDD. Scores pre- and post-elective were used to calculate relative improvement of participants. Results 120 students enrolled in DSDD, with 94 students meeting elective requirements. On average, 77.2% (SD = 6.2%) of students were female and 81.4% (SD = 12.9%) reported having prior experience with PWDD. Statistically significant (p<0.05) relative improvements were present in 9 of 10 scores for 2 of 5 years and all 10 in the other 3 years. Improved scores involved increases in confidence in interacting with PWDD, taking histories, recommending appropriate resources to families, and estimating developmental age. Conclusion DSDD may support acquisition of clinically relevant skills beyond those learned in the standard curriculum, as students consistently reported improvements in confidence across the same domains over 5 cohort years of this elective. The demonstration of maintained improvement is important because it may be translatable to future clinical practice and have implications towards optimizing outcomes for pediatric 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)0.004
metaresearch head score (Gemma)0.009
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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.092
GPT teacher head0.478
Teacher spread0.386 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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