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Record W4401552740 · doi:10.1111/scd.13052

Effectiveness of two visual‐pedagogical methods for toothbrushing skills in autistic children: A randomized clinical trial

2024· article· en· W4401552740 on OpenAlexaff
Matine Gharavi, Katayoun Salem, Rojin Adabdokht, Mahmoud Ghasemi

Bibliographic record

VenueSpecial Care in Dentistry · 2024
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineRepeated measures designRandomized controlled trialAutismDentistryTooth brushingVideo modelingToothbrushPsychologyTeaching methodMathematics educationModelling

Abstract

fetched live from OpenAlex

Abstract Aim This study aimed to compare the effectiveness of two visual pedagogy methods, video modeling and educational posters, on improving tooth‐brushing autonomy in 10–12‐year‐old children with mild autism. Methods Sixty‐four autistic children were randomly assigned to either the video or poster groups using the Rand function in Excel. Toothbrushing skills were divided into five stages: preparation, buccal, occlusal, lingual surfaces, and the end. These five stages comprised a total of 20 steps, with each step scored from 1 (not done at all) to 5 (done independently). The final score was calculated by averaging the scores of the five stages. The FONES method of toothbrushing was used for training. Follow‐up assessments were conducted after 1 and 3 months. The data were analyzed using SPSS V26, including t ‐tests, Mann–Whitney U tests, and repeated‐measures ANOVA. Results After 3 months, there were significant improvements in autonomy scores for both groups, with the video group showing greater benefits (4.37 ± 0.43) compared to the poster group (4.11 ± 0.49) ( p = .03), with an effect size of η 2 = .07. Conclusion Both video and poster methods were effective in improving tooth‐brushing skills, but video modeling was associated with a higher total autonomy score.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.527
Teacher spread0.469 · 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 designRandomized trial
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

Citations3
Published2024
Admission routes1
Has abstractyes

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