The impact of medical instructors' attitudes towards patients with developmental disabilities on undergraduate medical students in Northern Ontario
Bibliographic record
Abstract
The attitudes possessed by health care professionals are an important factor in patients with \ndevelopmental disabilities’ (DD) ability to access services, particularly in rural and remote \nregions, such as Northern Ontario. Despite the expressed need for greater education in medical \nschool on DD, students and providers often report discomfort when working with these patients. \nThis major paper aimed to answer the question: What impact do instructors and preceptors have \non medical students’ attitudes towards patients with developmental disabilities? Social Power \nTheory (French & Raven, 1959) was used to explore the themes in the literature. A total of 56 \narticles published between 1980 and 2019 were identified and reviewed for this analysis. Three \nmain themes were identified in the literature including: (1) Barriers to accessing health care, \nincluding both providers’ and students’ knowledge and attitudes; (2) Gaps in the health care \ncurricula and formal education; and (3) the power dynamic and culture of medical education. \nThe results of this review indicate that there is a lack of formal education and few clinical \nopportunities for students to learn about DD. Patients with DD have expressed a desire to be \nincluded in medical education in a professional capacity as an educator; this position of power \nmay provide them with an opportunity to improve students’ knowledge while reducing potential \nbiases. Although medical educators are experts in their field, they are often not formally trained \nas educators. The implications of this lack of formal training are that much of preceptors’ \nteaching styles are left to their discretion, which may include negative teaching approaches such \nas “ritual humiliation”
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 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".