Perceived Knowledge Gap of Public Health Officer in Ethiopia
Bibliographic record
Abstract
Abstract Background: Ethiopia has been training health officers as non-physician clinicians to meet the urgent public health service needs including preventive and health promotion as well as curative services. However, to date there is a lack of evidence of whether the curriculum content of the program meets the needs of the community. The purpose of this study was to explore the perception among health officers on whether the curriculum prepared them with what they need to know for practice when deployed to district hospitals and rural health centersMethod: A qualitative research design was used to explore health officer’s perceptions on the curriculum for clinical practice. The study was conducted in Dale district, Sidama region, southern Ethiopia. Data collection was done using semi-structured interview guide with open ended questions. The interview guide were developed using the first author’s experience as a health officer who recently graduated the program. All public health officers training graduates in the Dale districts, included in the study. Data analysis was done following a thematic analysis approach. The data was analyzed using ATLAS.ti qualitative data analysis software. Result: The study revealed a major curriculum-related gap which contributed to the perceived knowledge gap of public health officers. The main gap identified was the disproportional time allocation for very crucial courses of basic sciences and clinical practice during training. In addition the curriculum lacked a strict monitoring system of its implementation. Conclusion: Based on the result most participants believed the course content of the curriculum was very strong and crucial for public health officers. Some weakness of the curriculum were also identified which created the perceived knowledge gap of public health officers. The main weakness in the curriculum was that some of the courses were not proportionally allocated with adequate time for course contents like that of basic science and clinical courses.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".