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Record W1666893058

M&E Competencies in Support of the AIDS Response: A Sector-Specific Example

2014· article· en· W1666893058 on OpenAlexvenueno aff
Gillian Fletcher, Greet Peersman, William E. Bertrand, Deborah Rugg

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Context (archaeology)Political scienceHumanitiesMedicineNursingFamily medicineGeography
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.417
GPT teacher head0.482
Teacher spread0.065 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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