MétaCan
Menu
Back to cohort

Utilising assessment as a drive for changing teaching

2010· article· en· W1998033025 on OpenAlexaff
Valéria Góes Ferreira Pinheiro, Elcineide S Castro, L E A Troncon

Bibliographic record

VenueMedical Education · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsObjective structured clinical examinationRetrainingCurriculumMedical educationChristian ministryTest (biology)Educational measurementPsychologyTeaching methodMedicineMathematics educationPedagogy

Abstract

fetched live from OpenAlex

The release in 2001 of the National Curricula Guidelines by the Ministry of Education of Brazil prompted medical colleges to increase student training in clinical skills as well as learning of cognitive content. At our institution, new strategies for student learning and assessment are being introduced in order to conform to the new guidelines. As faculty staff and clinical teachers are usually resistant to changes in teaching, but sensitive to assessment issues, we decided to implement an objective structured clinical examination (OSCE) in the pneumology module as a strategy through which to adapt curriculum and teaching methods to the new guidelines. The OSCE planning, which demanded the definition of the skills to be assessed, was aligned to the revision of educational objectives. This led to the redefinition of teaching methods and to the development of new instructional materials. Complementary actions involved informing the institution, establishing the team and retraining the instructors (n = 10) for both assessment and teaching. The strategy was completed with the implementation of structured examinations for two classes (n = 36 and n = 37 students). Each test comprised six stations (three with simulated patients [SPs]) covering the most relevant clinical skills. After the examination students were given feedback on their performances, which had been recorded on video. Analysis of overall examination results led to the identification and subsequent correction of teaching deficiencies in the module. At the end of the module, after the examination, students completed structured questionnaires evaluating both the module and the OSCE. The examination was also evaluated by instructors and SPs. The reshaped module was considered to be good or excellent by 57% of the students in terms of its achievement of the teaching goals. The OSCE was considered to be an appropriate assessment method by 79% of students; 88% felt that the content and tasks addressed in the assessment were aligned with the material that had been taught. The instructors regarded the examination as feasible and appropriate in terms of the planning, location and resources used. The SPs found that the training was easy and, although they complained about the repetition of exhaustive work, expressed the belief that they had contributed to student learning. We concluded that the introduction of OSCEs in the pneumology module of the medical curriculum at our institution is feasible and effective as a tool for skills assessment and that it can be seen as positive by students, instructors and SPs. The strategy of examination implementation was important because it allowed us to review both content and teaching methods and to train teachers in assessment. Maintenance of this project may enable us to continue a more accurate evaluation of its influence on student performance of clinical skills in pneumology and to extend this approach to other modules in the medical course. Notwithstanding the limitations of our work, this initiative indicates that curriculum and teaching methods can be effectively improved by using modifications in assessment as a driving force.

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)0.043
metaresearch head score (Gemma)0.091
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.091
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.011
Scholarly communication0.0120.008
Open science0.0030.013
Research integrity0.0020.004
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 head0.010
GPT teacher head0.421
Teacher spread0.411 · 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 designQualitative
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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueMedical EducationSame topicInnovations in Medical EducationFrench-language works237,207