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Record W4387416040 · doi:10.2196/45311

Effects of Distance-Learning Strategies in Dental Fixed Prosthodontics Amidst the COVID-19 Pandemic: Cross-Sectional Questionnaire Study on Preclinical Dental Students’ Perspective

2023· article· en· W4387416040 on OpenAlexafffundvenue
Xi Yue Zhang, Anelyse Arata, Sheila Butler

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsWestern University
FundersSchulich School of Medicine and DentistrySchulich School of Medicine and Dentistry, Western University
KeywordsLikert scaleCoronavirus disease 2019 (COVID-19)PsychologyFeelingMedicinePandemicAnxietyCourseworkMedical educationCross-sectional studyClinical psychologySocial psychologyDiseasePathologyPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: COVID-19's high transmissibility led to gathering restrictions where dental schools experienced disruptions due to restrictions on attending in-person lectures and limitations placed on applied preclinical and clinical activities. Students not only had to rapidly switch to digital technology-based learning (TB-learning) modules but also experienced high levels of social isolation and anxiety around virus transmission. OBJECTIVE: This study aims to evaluate the preclinical students' perception of switching TB-learning modules amidst the COVID-19 pandemic, identifying which module parameters were associated with strong student outcomes. METHODS: A web-based survey of 39 Likert scale questions was delivered to preclinical dental students (Western University) to evaluate students' perceptions concerning TB-learning, fear amidst the COVID-19 pandemic, and the impact on their preclinical training. A Spearman rank correlation coefficient was determined to estimate the relationship between 2 variables in isolation (P=.01). An ordinal regression analysis was performed on variables of interest to determine how module variables (typically within the instructor's control) influenced the student outcomes (P=.05). RESULTS: The response rate was 30% (n=39). TB-learning was considered vital (34/39, 87.2%) as the students' education improved (18/39, 46.2%). However, 53.8% (n=21) of students showed increased difficulties in retaining, visualizing, or understanding the materials using TB-learning, and 64.1% (n=25) found it more difficult to concentrate than in in-person classes. In total, 79.5% (n=31) of students showed different levels of agreement about feeling fatigued from TB-learning. Through Spearman ρ correlation analysis, the quality of questions in quizzes (ρ=0.514; P<.001), relevant handouts (ρ=0.729; P<.001), and high-quality audiovisuals (ρ=0.585; P<.001) were positively correlated with students responding that the modules were useful to preclinical training. Similarly, good organization (ρ=0.512; P<.001), high-quality questions in quizzes (ρ=0.431; P=.01), and relevant handouts (ρ=0.551; P<.001) were positively correlated with web-based classes as an effective way to learn. In total, 91.6% (n=36) of the students agreed that COVID-19 was a dangerous disease, whereas 53.8% (n=21) showed different levels of agreement that they were afraid to be infected personally, and 69.2% (n=27) feared passing COVID-19 along to family and friends. A total of 82.1% (n=32) of the students showed that COVID-19 impacted their overall learning process and had a negative impact on their practical preclinical training (31/39, 79.5%). CONCLUSIONS: The students found a difference between TB-learning and face-to-face learning methods, where the students perceived fatigue toward the web-based method with difficulty concentrating and visualizing the subject. Moreover, there was a consensus that COVID-19 itself affected the students' overall learning process and preclinical training. As dental schools continue implementing TB-learning into their curriculum, this investigation identifies the students' struggles with the paradigm shift. In an effort to improve TB-learning, this work highlights 4 variables (organization, quizzes, quality handouts, and quality audiovisuals) within the control of instructors that can help improve content deliverance, improving the students' experience.

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)0.003
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.144
GPT teacher head0.549
Teacher spread0.405 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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