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Development of an undergraduate curriculum in obstetrical simulation

2010· article· en· W2085651790 on OpenAlexaff
Glenn Posner, Amy Nakajima

Bibliographic record

VenueMedical Education · 2010
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsHealth Canada
Fundersnot available
KeywordsCurriculumObstetrics and gynaecologyMedical educationSpecialtyPresentation (obstetrics)Session (web analytics)Experiential learningMedicineObstetricsPsychologyFamily medicinePedagogyPregnancyComputer science

Abstract

fetched live from OpenAlex

Because of the sensitive nature of the specialty, medical students can often be marginalised during their obstetrics rotation. Opportunities for hands-on experience are often lacking, yet we know that experiential learning increases understanding and activates trainees. As the use of simulation becomes more common in postgraduate medical education, the question of its application to the undergraduate experience becomes relevant. A curriculum was designed for a simulation-based workshop during the first week of the obstetrics clerkship to instruct students in the diagnosis of labour and intrapartum management. Prior to the development of this curriculum, formal teaching for clerkship students in obstetrics and gynaecology at our institution consisted solely of didactic lectures. The objective of this innovation was to assess the effectiveness of a new simulator-based curriculum on learning during the obstetrics clerkship. As a secondary outcome, it was hoped that these sessions would engage the students and serve to lessen their anxiety at the start of their rotation, optimise their experiences, and foster an interest in pursuing postgraduate training in obstetrics and gynaecology. A structured simulation-based curriculum was developed in accordance with the educational objectives of the US-based Association of Professors of Gynecology and Obstetrics. A total of 110 students from the class of 2010 at our university attended a small-group session at the simulation centre, which incorporated the use of a high-fidelity obstetrical manikin. The curriculum is based on a single obstetrical patient whose clinical course is followed by the students from presentation in the obstetrics assessment unit to eventual delivery and postpartum care. The students are given the opportunity to examine the patient several times during the course of her labour to assess her dilation. During the discussion, they are able to handle forceps, vacuum extractors, amniotomy hooks and scalp electrodes. The workshop culminates in a mock delivery managed by each student. The students completed a pre-test just prior to the teaching session and a post-test immediately after the session. This test addressed definitions, risk factors and management decisions relevant to basic intrapartum care and mirrored the content of the workshop. Written feedback was solicited from the first group of students. Performance on the two tests was compared using inferential and descriptive statistics. Following the session, the median student score increased significantly from 40.0% (14/35, standard deviation [SD] = 5.5) on the pre-test to 71.4% (25/35, SD = 4.3) on the post-test (P < 0.01). Scores ranged from 2 to 29 out of 35 on the pre-test and from 9 to 33 out of 35 on the post-test. Thirty-eight students rated ‘the overall quality of the session’ and ‘the appropriateness of the topic’ on a 10-point Likert scale. Mean scores were 8.7/10 for quality and 9.1/10 for appropriateness. High-fidelity simulation can be a useful adjunct to the current education of medical students. A structured, interactive simulation curriculum in obstetrics is an effective teaching approach and is well received by medical students.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.246
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.401
Teacher spread0.380 · 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 teacher head, 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

Citations6
Published2010
Admission routes1
Has abstractyes

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