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Record W3193434868 · doi:10.5812/semj.110977

A Differential Item Functioning (DIF) Study of the Infection Control Standard Isolation Precaution Instrument Across Gender and Major Among Healthcare Workers

2021· article· en· W3193434868 on OpenAlexaff
Amin Mousavi, Mohsen Momeni, Mina Danaei, Mehrdad Askarian

Bibliographic record

VenueShiraz E-Medical Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDifferential item functioningHealth careStatisticPsychologyIsolation (microbiology)Test (biology)Infection controlClinical psychologyMedicinePsychometricsItem response theoryStatistics

Abstract

fetched live from OpenAlex

Background: Measuring healthcare workers’ (HCWs) knowledge, attitudes, and practices (KAPs) regarding isolation precaution is essential for infection control which needs a valid and reliable instrument. Objectives: This study aimed to assess differential item functioning (DIF) across gender and major for the knowledge and practice items of the questionnaire, previously designed in Shiraz, Iran. Methods: This cross-sectional survey was conducted on 1070 participants (males/females: 306/764; medical students/nurses: 466/624). The study instrument had three subscales with nine questions for each KAP subscale. The Mantel-Haenszel (MH) statistic was used. The DIF and differential test functioning (DTF) analyses were also performed in this study. Results: There were very similar DIF outcomes for the knowledge and practice subscales, with one or two items indicating moderate DIF but comparable total scores across genders. Across majors, several items showed large DIF for both subscales. It was found that large DTF affects major for both subscales. Conclusions: Our findings indicated large DIF and DTF levels of the questionnaire among medical students and nursing groups. More attention should be paid when developing the items. This study shows the importance of paying attention to valid evidence for instruments developed within the field of healthcare.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.028
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.325
Teacher spread0.299 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueShiraz E-Medical JournalSame topicInfection Control in HealthcareFrench-language works237,207