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Record W3196033429 · doi:10.2196/26980

Knowledge, Attitudes, and Practices Regarding COVID-19 Among Health Care Workers in Public Health Facilities in Eastern Ethiopia: Cross-sectional Survey Study

2021· article· en· W3196033429 on OpenAlexvenueno aff
Alinoor Mohamed Farah, Tahir Yousuf Nour, Muse Obsiye, Mowlid Akil Aden, Omar Moeline Ali, Muktar Arab Hussein, Abdullahi Bedel Budul, Muktar Omer Omer, Fentabil Getnet

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsSomaliLikert scaleCross-sectional studyPublic healthHealth careScale (ratio)MedicineFamily medicineEnvironmental healthPsychologyNursingGeographyEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: On March 13, 2020, Ethiopia reported the first confirmed case of COVID-19 in Addis Ababa. COVID-19 is likely to overwhelm an already-fragile health care delivery system and reduce the availability of essential health services. This analysis of data from the Somali Region of Eastern Ethiopia on health care workers' (HCWs) knowledge, attitudes, and practices regarding the prevention and control of COVID-19 may be used in planning health education programs about the emerging viral disease. OBJECTIVE: This study aimed to investigate the knowledge, attitudes, and practices of HCWs regarding COVID-19 infection. METHODS: This cross-sectional study was conducted among HCWs in three public health facilities in the Somali Region, Eastern Ethiopia. A self-administered questionnaire was shared with all HCWs working at the public health facilities. A total of 15 knowledge questions were scored as 1 or 0 for correct or incorrect responses, respectively. A total of 14 practice questions were scored on a 3-point scale from 1 ("always") to 3 ("never"). A total of six attitude questions were rated on a 5-point Likert scale, in a negative dimension, as follows: 1 ("strongly agree"), 2 ("agree"), 3 ("neutral"), 4 ("disagree"), and 5 ("strongly disagree"). Mean scores were calculated and used as a cut point to dichotomize the outcome variables (>13.7 indicated good knowledge, <18.8 indicated good practices, and ≤10.5 indicated favorable attitudes). We used t tests and analyses of variance (ie, F tests) to analyze the mean score differences of knowledge, attitudes, and practices between the independent variables. Spearman correlation was used to assess the relationship between mean knowledge and attitude scores. RESULTS: Of the 686 HCWs approached, a total of 434 HCWs responded (63.3% response rate). The mean age of the participants was 27.6 (SD 5.3) years, and the majority of the participants were male (293/434, 67.5%). The mean knowledge score was 13.7 (SD 2.6), and 73.3% (318/434) of participants had sufficient knowledge. The mean attitude score was 10.5 (SD 4.1), and 54.8% (238/434) of the participants had a good attitude toward COVID-19. The mean practice score was 18.8 (SD 5.8), and 61.5% (267/434) of the participants practiced precautionary measures to prevent COVID-19. There was a negative correlation between knowledge and attitude scores (r=-0.295, P<.001) and between knowledge and practice scores (r=-0.298, P<.001). CONCLUSIONS: The overall levels of knowledge and practice were relatively better than the attitude level. This highlights the need to implement strategies that enhance the positive attitudes and safe practices of the HCWs for better containment of the pandemic and supporting of essential health care services.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.464
GPT teacher head0.619
Teacher spread0.154 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2021
Admission routes1
Has abstractyes

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