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Record W2518459073 · doi:10.1097/der.0000000000000215

Return to Work for Nurses with Hand Dermatitis

2016· article· en· W2518459073 on OpenAlexaffvenue
Jennifer Chen, Pilar García‐Gómez, Irena Kudla, Joel G. DeKoven, D. Linn Holness, Sandra Skotnicki

Bibliographic record

VenueDermatitis · 2016
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsUniversity of TorontoPublic Health OntarioSt. Michael's HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineIntervention (counseling)NursingHealth careMEDLINEFamily medicinePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Occupational skin disease is common in healthcare workers. If the healthcare worker develops moderate to severe dermatitis, return to work (RTW) may be challenging. OBJECTIVES: The study objectives were to review the impact of an RTW program on the work status of nurses with occupational hand dermatitis and to identify successful intervention methods and strategies. METHODS: Nurses who received RTW services at a tertiary occupational medicine clinic were identified, and information related to their diagnosis and RTW was abstracted from their charts. RESULTS: Eighteen nurses with irritant hand dermatitis who received RTW services were identified. Twelve nurses (67%) were performing administrative duties because of their skin condition when admitted to the RTW program, and others were performing patient care with modifications. A graduated RTW trial was commonly implemented with optimized skin care management and monitoring by physicians and the RTW coordinator. Upon discharge, 14 nurses (78%) had returned to their nursing roles with direct patient care, 3 (17%) were working as nurses in non-patient care roles, and 1 (6%) was on permanent disability. CONCLUSIONS: A graduated RTW trial to reduce cumulative irritant exposure is a crucial strategy to facilitate nurses' transition back to work and to maintain direct patient care nursing roles.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.520

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 communication0.0000.000
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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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 designNot applicable
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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