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Record W2404654282

Evaluation of elementary school teachers' knowledge and attitudes about immediate emergency management of traumatic dental injuries.

2012· article· en· W2404654282 on OpenAlexaboutno aff
Şule Bayrak, Emine Şen Tunç, Erhan Sarı

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldHealth Professions
TopicDental Trauma and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDental traumaQuarter (Canadian coin)First aidSchool teachersEmergency managementFamily medicineMedical emergencyDentistryMathematics educationPsychology
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: To investigate teachers' knowledge and attitudes about emergency management of traumatic dental injuries (TDIs) in children. MATERIALS AND METHODS: A total of 764 teachers from 13 elementary schools were included in the study. Data were collected using a self-reporting questionnaire in which teachers were asked about demographic information, previous experience with dental trauma, first-aid training, knowledge of emergency management and how they would respond to two hypothetical TDI cases. RESULTS: Of the 764 participants, 550 (71.4%) returned the questionnaire; of these, 309 (56.2%) were female and 241 (43.8%) were male. While 297 teachers reported having had first-aid training, only 13 (4.4%) of them reported emergency management of TDIs being covered in this training. Less than half of respondents (47.5%, n = 261) correctly answered the question on the appropriate response to a TDI involving a fractured tooth and only one-quarter of respondents (25.4%, n=140) correctly answered the question on the appropriate response to a TDI involving an avulsed tooth. CONCLUSION: The results of this study demonstrated teachers' low level of knowledge about the emergency treatment of TDIs in schoolchildren, suggesting that educational programmes are needed to improve proper emergency management of TDIs by teachers.

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)0.002
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 score0.156
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.110
GPT teacher head0.425
Teacher spread0.316 · 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

Citations29
Published2012
Admission routes1
Has abstractyes

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