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Record W3195613603 · doi:10.22037/jcpr.v6i2.34206

Attitude of Iranian Orthopedic Residents towards Online Educating Programs in COVID-19 Pandemic

2021· article· en· W3195613603 on OpenAlexaff
Reza Jahanshahi, Adel Ebrahimpour, Hossein Mohebbi, Shiva Momen, Mehrdad ghobadi, Javad Ahmadlou, Peyman Zia, Mehdi Aarabi, Seyyed-Mohsen Hosseininejad

Bibliographic record

VenueJournal of Clinical Physiotherapy Research · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsToronto East General HospitalUniversity Health Network
Fundersnot available
KeywordsPandemicMedical educationCoronavirus disease 2019 (COVID-19)Online learningThe InternetDistance educationMedicineContinuing medical educationCross-sectional studyComputer-assisted web interviewingPsychologyFamily medicineContinuing educationPedagogyMultimediaComputer science

Abstract

fetched live from OpenAlex

Objective The current COVID-19 pandemic impose many challenges including maintain educational programs delivery obstacles. The aim of this study was to investigate orthopedics residents’ opinion towards using online methods to sustain medical education. Methods A cross sectional study applying an 8-item questionnaire was performed to investigate 150 Iranian residents’ attitudes towards online medical learning programs. Results One-hundred thirty two residents (88%) replied back the questionnaire. 89% of the participants reported they use online methods; most frequent method was online webinars by 84%. They frequently used Skyroom (76%) to take part in the educational events. Satisfaction rate for webinar was reported as well as 8.2. Weak internet connection and speed was the main problem reported by the participants. 91.8% of the residents supposed continuing the online education would be useful. Conclusion Orthopedic residents believed that Online educating programs would be favorable and useful to continue after the current pandemic.

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)0.011
metaresearch head score (Gemma)0.003
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 score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.641
GPT teacher head0.699
Teacher spread0.058 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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