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Record W2734344860 · doi:10.5430/jha.v6n4p46

Knowledge and practices among registered nurses on occupational hazards in Onandjokwe Health District: Oshikoto region, Namibia

2017· article· en· W2734344860 on OpenAlexvenueno aff
Julia Amadhila, Jacoba Marieta van der Vyver, Daniel Opotamutale Ashipala

Bibliographic record

VenueJournal of Hospital Administration · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational safety and healthMedicineDescriptive statisticsNursingPopulationFamily medicineEnvironmental healthPsychology

Abstract

fetched live from OpenAlex

Objective: This study sought to explore and describe the extent of the knowledge on occupational hazards amongst registered nurses in Onandjokwe Health District.Methods: This study used a quantitative research design utilizing a survey by means of a questionnaire. The population of the study consisted of randomly selected registered nurses who were in direct contact with patients. Data was gathered using questionnaires, utilized for descriptive statistics and evaluated with quantitative, computerised statistical techniques using. Statistical Package for the Social Scientists (SPSS).Results: The findings of this study revealed that the majority of registered nurses have knowledge on occupational hazards, yet a few number (24%) have insufficient knowledge. The findings also revealed that information and support is provided to some (22%) nurses.Recommendations: Recommendations made based on the findings of this study include regular training and educational meetings to enhance occupational safety, develop/introduce policies and guidelines or strategies on all aspects related to occupational hazards/safety.Conclusions: Most (76%) of the registered nurses have knowledge on the ways that they can be exposed to occupational hazards in the following areas: such as handling of sharp instruments, lifting of patients, exposure to psychological problems due to frustrations, and exposure airborne diseases.

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)0.004
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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.164
GPT teacher head0.539
Teacher spread0.374 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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