Routine Medical Checkup Knowledge, Attitude and Practice among Health Care Workers in a Tertiary Health Facility in Calabar, Cross River State, Nigeria
Bibliographic record
Abstract
INTRODUCTION: Routine medical checkup is seen as effective in preventing illness and promoting health as well as reducing morbidity and mortality. The aim of this study was to determine the knowledge, attitude and practice of routine health check-up among health care workers (Doctors, Nurses, Radiographers, Lab scientists, Pharmacists, Medical record officers and Health assistants) in a tertiary health facility in Calabar, Nigeria. METHODS: A descriptive survey design was adopted for the study. Stratified random sampling technique was used to select sample size of 318. Data was collected through a structured interviewer questionnaire with the reliability coefficient of 0.83. Data generated were analyzed using statistical package for social sciences (SPSS v.20). The significance of the hypothesis was tested using Chi – Square statistics at <0.05 level of significance. RESULT: From the findings, majority of the respondents, 295 (92.8%) had good knowledge of routine health checkup. Most respondents, 205 (64.5%) had positive attitude towards routine health checkup. And a few respondents, 147 (46%) practiced routine health checkup. Hypothesis revealed that the calculated X2 value (5.92a) was greater than the critical X2 value of 3.84 at <0.05 level of significance showing that there was a significant relationship between Doctors and Nurses knowledge and practice of routine health checkup. CONCLUSION: In conclusion, health workers had good knowledge and positive attitude towards routine health checkup. Poor practice of routine medical checkup was also observed. Hence, the researchers recommended that regular seminars and mandatory annual medical examinations should be organized for health workers in all the departments in the hospital to improve their knowledge and practice of routine health check-up. There is need to study the factors associated with practice of medical checkup among Doctors and Nurses in the study area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".