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Record W2429941148 · doi:10.1097/der.0000000000000155

Glove Use and Glove Education in Workers with Hand Dermatitis

2016· article· en· W2429941148 on OpenAlexaffvenue
Kyle Rowley, Daana Ajami, Denise Gervais, Lindsay Mooney, Amy Belote, Irena Kudla, Sharon Switzer‐McIntyre, D. Linn Holness

Bibliographic record

VenueDermatitis · 2016
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicinePersonal protective equipmentHand dermatitisOccupational exposureDemographicsHand eczemaPhysical therapyContact dermatitisMedical emergencyAllergyPathologyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

BACKGROUND: Occupational skin diseases are common. The occurrence of occupational skin diseases represents a failure of primary prevention strategies that may include the use of personal protective equipment, most commonly gloves. OBJECTIVE: The objective of this study was to describe current glove use and education practices related to gloves in workers being assessed for possible work-related hand dermatitis. METHODS: Participants included consecutive patients being assessed for possible work-related hand dermatitis. A self-administered questionnaire obtained information on demographics, workplace characteristics and exposures, glove use, and education regarding gloves. RESULTS: Ninety percent of the 105 participants reported using gloves. Only 44% had received training related to glove use in the workplace. Major gaps in training content included skin care when using gloves, warning signs of skin problems, and glove size. If the worker indicated no glove training received, the majority reported they would have used gloves if such training was provided. CONCLUSIONS: Although the majority of workers being assessed wore gloves, the minority had received training related to glove use. Particular gaps in training content were identified. Those who had not received training noted they would likely have used gloves if training had been provided.

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)0.000
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 score0.101
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

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