M&E Competencies in Support of the AIDS Response: A Sector-Specific Example
Bibliographic record
Abstract
The Joint United Nations Programme on HIV/AIDS (UNAIDS) led a consultative process to develop a self-assessment tool for HIV monitoring and evaluation (M&E) leadership competencies. The tool seemed fit-for-purpose in M&E staff recruitment and professional development. The willingness to use the self-assessment was related to the pragmatic and reality-based nature of the tool. A competency-based approach to M&E training was well accepted by professionals working at national and service-delivery levels. However, there is a need to update the HIV M&E competencies to adapt to specific M&E challenges in the broader context of aid effectiveness and to reflect a maturing evaluation profession. Le Programme Commun des Nations Unies sur le VIH/SIDA (ONUSIDA) a mene un processus de consultation pour elaborer un outil d’auto-evaluation pour les competences en leadership en suivi et evaluation (S&E) du VIH. L’outil semble apte au but en recrutement du personnel et developpement professionnel pour suivi et evaluation. La volonte d’utiliser l’auto-evaluation a ete liee a la nature pragmatique et fondee sur la realite de l’outil. Une approche axee sur les competences en formation S&E a ete bien recue par les professionnels qui travaillent a des niveaux nationaux de prestation de services. Cependant, il est necessaire d’actualiser les competences VIH S&E pour s’adapter aux defis specifiques au S&E dans le contexte plus large de l’efficacite de l’aide et pour refleter la maturation de la profession d’evaluation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".