Experiences establishing a new speech-language pathology training program in Ethiopia, a resource-limited setting: Lessons learned
Bibliographic record
Abstract
PURPOSE: Ethiopia is the second most populous country in sub-Saharan Africa. While Ethiopia's health care system includes primary health centres, general, and specialised hospitals, allied health care like speech-language pathology was not available until 2003. This article was written with the aim of sharing the experience of establishing speech-language pathology as a profession and the first speech-language pathology training program in Ethiopia. METHOD: In this paper, we retrospectively examine how the leadership of local stakeholders, a multidisciplinary team, and the development of a professional infrastructure led to the success of the program. The authorship group, who were involved in the program from inception to implementation, share their experiences. RESULT: The speech-language pathology undergraduate program at Addis Ababa University graduated its first class in 2019. Plans to grow the training program at the graduate level are ongoing. CONCLUSION: This novel program, grown from several international partnerships, is an example of how low- and middle-income countries can improve access to the service providers necessary to treat their populations.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".