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Record W2766937575 · doi:10.47339/ephj.2017.83

Knowledge comparison between group childcare centres and family childcares on sanitation of toys

2017· article· en· W2766937575 on OpenAlexfundvenueno aff
Yun Ha Hwang, Environmental Health BCIT School of Health Sciences, Helen Heacock, Fred Shaw

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPediatric health and respiratory diseases
Canadian institutionsnot available
FundersBritish Columbia Institute of Technology
KeywordsSanitationHygieneEnvironmental healthBusinessPsychologyMedicine

Abstract

fetched live from OpenAlex


 Background: Childcare facilities (CCFs) are known to have a high potential risk of exposure and transmission of infectious diseases through contact surfaces, such as toys. Research to date suggests that toys are a potential source of cross-infections in CCFs, especially when childcare providers do not practice proper hygiene. Currently, there is a lack of knowledge on the differences in sanitation methods of toys between group and family CCFs. This study compared knowledge of group and family CCFs regarding how to sanitize toys. Methods: Self-administered surveys were distributed to group and family CCFs in Surrey, BC via e-mail. The survey was used to assess the knowledge of childcare providers on sanitation of toys. The survey was evaluated using a scoring system. In addition, each participant answered descriptive questions, such as the existence of sanitation plans and toy cleaning and sanitizing schedules. Results: Group and family CCFs showed no statistically significant differences in knowledge levels on sanitation of toys. The mean score of the knowledge level of group and family CCFs was 65% and 55% respectively. Conclusion: Childcare providers in CCFs play a key role in properly sanitizing toys and preventing transmission of infectious diseases between children. Recognizing knowledge gaps in sanitation can lead to policy development as well as improved educational programs.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
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 head0.106
GPT teacher head0.418
Teacher spread0.312 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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