Traditional healers’ roles, and the challenges they face in the prevention and control of local disease outbreaks and pandemics: The case of the East Gojjam Zone in northwestern Ethiopia
Bibliographic record
Abstract
Objectives The main objective of this study was to assess the roles of traditional healers and the challenges they face in the of prevention and control of both local disease outbreaks and the COVID-19 pandemic, with a special emphasis on the work of traditional healers and healing centers in the East Gojjam Zone in northwestern Ethiopia, between 2020 and 2021. Methods From 25 February 2021 to 2 May 2021, a mixed-methods study (qualitative techniques combined with a quantitative approach) was carried out. The study was conducted by traditional healers and at healing centers in the East Gojjam Zone. The quantitative sample size was calculated based on the assumption of a single population proportion formula. As part of the qualitative research, levels of data saturation were continuously monitored, and were used to determine what the maximum number of study participants should be. Traditional healers and their clients were the study units for the quantitative component, whereas traditional healthcare providers (of all types) and religious leaders were purposively selected as the study units for the qualitative part. Descriptive and inferential statistical methods of analysis, and narrative- and content-wise methods of analysis, were used for the quantitative and qualitative components of this study, respectively. Results The quantitative findings of this study showed that 64.27% of respondents (95% CI 59.53% to 68.74%) had a good awareness of regional disease outbreaks and of the COVID-19 pandemic. Only 9.59% of people had a positive opinion regarding local disease outbreaks, the COVID-19 pandemic, and the preventive and control measures that were employed in response to these (95% CI 7.11% to 12.83%). In addition, this study revealed that a small percentage of participants (i.e., 2.16%) used traditional control and preventive measures in response to the COVID-19 pandemic and local disease outbreaks. Conclusion Less than one-tenth of respondents had a favorable attitude toward local disease outbreaks, the current COVID-19 pandemic, and the preventive and control measures that were employed in response to these. In addition, only a small number of study participants had actually used conventional control and preventive measures in response to local disease outbreaks and the COVID-19 pandemic. Nearly two-thirds of respondents had a good understanding of the preventive and control measures that were employed in response to local disease outbreaks and the COVID-19 pandemic.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".