MétaCan
Menu
Back to cohort
Record W2119307511 · doi:10.5539/jel.v3n4p26

Video Review in Self-Assessment of Pharmacy Students’ Communication Skills

2014· article· en· W2119307511 on OpenAlexvenueno aff
Lucio R. Volino, Rolee Pathak Das

Bibliographic record

VenueJournal of Education and Learning · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySelf-assessmentSession (web analytics)Medical educationCurriculumPerceptionPharmacyCommunication skillsNonverbal communicationMedicinePedagogyNursingComputer scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

The objectives of this study were to develop a student self-assessment activity of a video-recorded counseling session and evaluate its impact on student self-perceptions of specific communication skills. This activity was incorporated into a core-communications course within the third professional year of a Doctor of Pharmacy curriculum. Student counseling sessions were video-recorded and released to students for self-assessment review. After watching their recorded counseling sessions, students completed an eight-question, paper-based survey which evaluated the impact of self-assessment on communication skill perceptions. Most students (95.6%) agreed or strongly agreed that their self-assessment process was valuable in developing their communication skills. The greatest change in assessment was associated with the use of eye contact (49.5%). Approximately 40% of students noted changes in perceptions for appropriate rate of speech (42.1%), voice volume (39.8%), and facial expressions (37.2%). Overall, self-assessment of video-recorded counseling sessions impacted students’ perceptions of both verbal and non-verbal communication skills.

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)0.008
metaresearch head score (Gemma)0.032
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.014
GPT teacher head0.440
Teacher spread0.427 · 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".

Quick stats

Citations10
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Education and LearningSame topicInnovations in Medical EducationFrench-language works237,207