The ‘Teachers Diploma Program’ in Zambian Government Schools: A Baseline Qualitative Assessment of Teachers’ and Students’ Strengths and Challenges in the Context of a School-Based Psychosocial Support Program
Bibliographic record
Abstract
In Zambia, as elsewhere throughout sub-Saharan Africa, orphaned and vulnerable children (OVC) face multiple physical, emotional, social and psychological challenges which often negatively affect opportunities for educational attainment. REPSSI (Regional Psychosocial Support Initiative), in collaboration with, the University of Cape Town and other African academic institutions, developed the Teachers’ Diploma Program as part of the Mainstreaming Psychosocial Care and Support into Education Systems to provide teachers and school administrators with the knowledge and skills to provide needed support to students and enhance their learning environments. During initial implementation of the Teachers’ Diploma Program in Zambia (2013-2016), qualitative data was collected as a part of larger outcomes and process evaluation. In the current paper, these qualitative data are presented to describe baseline challenges and strengths within the Zambian government school system and early indicators of change during the first ten months of program implementation. These in-depth data provide both teachers’ and students’ experiences and perspectives and are being utilized to further strengthen the Teachers’ Diploma Program as the Zambian Ministry of Education, Science, Vocational Training and Early Childhood moves forward with plans to implement the training at a national level in colleges of teacher education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".