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Portable Digital Video Instruction in Predoctoral Education of Child Behavior Management

2007· article· en· W2184230322 on OpenAlexaff
James R. Boynton, Lynn Johnson, S M Hashim Nainar, Jan C.‐C. Hu

Bibliographic record

VenueJournal of Dental Education · 2007
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsUniversity of Alberta
FundersUniversity of Michigan
KeywordsCurriculumClass (philosophy)MultimediaDigital videoMedical educationIntervention (counseling)Exploratory researchMedicinePsychologyComputer sciencePedagogyNursing

Abstract

fetched live from OpenAlex

The goals of this exploratory study were to determine students' assessment of portable digital video instruction (using the Apple iPod) and to compare examination performance among groups of predoctoral dental students who did and did not utilize portable digital video instruction as a supplement to a conventional pediatric behavior management lecture. Dental students received a one-hour lecture on communication with the parent and child patient as part of their regular sophomore pediatric dentistry curriculum. Digital audio and digital video versions of this lecture were made available to all 113 students in the class. Eleven student volunteers were loaned portable digital video players (the iPod) containing the lecture for a two-week period. Upon completion of the study period, the entire class participated in an anonymous fifteen-minute post-intervention written assessment including a thirteen-item examination covering lecture material. Students who had used the iPod to review the digital video lecture material favored this medium as a pedagogical instrument and as a group performed significantly better on the examination than those who had not reviewed the digital material (p=0.034). In conclusion, portable digital instructional videos may be a useful educational methodology to help predoctoral dental students acquire knowledge in pediatric behavior management.

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)0.000
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 score0.377
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.403
Teacher spread0.384 · 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

Citations12
Published2007
Admission routes1
Has abstractyes

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