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Record W2151771699 · doi:10.3109/0142159x.2012.660213

Using an objective structured video exam to identify differential understanding of aspects of communication skills

2012· article· en· W2151771699 on OpenAlexafffund
Danielle Baribeau, Ilya Mukovozov, Thomas F. Sabljic, Kevin W. Eva, Carl B. deLottinville

Bibliographic record

VenueMedical Teacher · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityUniversity of Toronto
FundersMcMaster University
KeywordsBlueprintCommunication skillsCurriculumMedical educationObjective structured clinical examinationExperiential learningHealth carePsychologyComputer scienceMultimediaMedicinePedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Effective communication in health care is associated with patient satisfaction and improved clinical outcomes. Professional schools increasingly incorporate communication training into their curricula. The objective structured video exam (OSVE) is a video-based examination that provides an economical way of assessing students' knowledge of communication skills. This study presents a scoring strategy that enables blueprinting of an OSVE to consensus guidelines, to determine which aspects of communication skills create the most difficulty for students to understand and to what degree understanding improves through experiential communication skills training. METHODS: Five interactions between a healthcare professional and client were scripted and filmed using standardized patients. The dialogues were mapped onto the Kalamazoo consensus statement by having five communication experts view each video and identify effective and ineffective use of communication skills. Undergraduate students enrolled in a communications course completed an OSVE on three occasions. RESULTS: A total of 79 students completed at least one testing session. The scores assigned supported the validity of the scoring strategy as an indication of knowledge growth. Considerable variability was observed across Kalamazoo sub-domains. CONCLUSION: With further refining, this scoring approach may prove useful for educators to tailor their education and assessment practices to specific consensus guidelines.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.327
GPT teacher head0.494
Teacher spread0.166 · 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.

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

Citations31
Published2012
Admission routes2
Has abstractyes

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