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Record W6977764754 · doi:10.7910/dvn/23649

Guatemala (2013): Assessment of Quality of Service Provision to Most-at-Risk Populations by Private Sector Providers in Central America. Round 1.

2013· dataset· en· W6977764754 on OpenAlexaboutno aff

Bibliographic record

VenueHarvard Dataverse · 2013
Typedataset
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPrivate sectorQuality (philosophy)PopulationQuarter (Canadian coin)Service providerProgram evaluationService (business)Health servicesService delivery framework

Abstract

fetched live from OpenAlex

The specific objectives of this evaluation are to: Assess the quality of service provision to MARPs by private-sector health-service providers participating in the HIV Combination Prevention Program for MARPs funded by USAID in Central America (Belize, Costa Rica, El Salvador, Guatemala, Nicaragua and Panama). Compare the quality of services provided to MARPs to the quality of services provided to the general population by private-sector health-service providers participating in the Program in the countries of interest. Identify areas for improvement in service provision to MARPs at participating facilities and provide recommendations. This is a descriptive, mystery client study that will be conducted annually over the course of the USAID HIV Combination Prevention for MARPs Program. We plan to begin the first annual assessment process in the first quarter of 2012. We will use mystery clients to gather data. Mystery clients will visit selected health facilities for HIV VCT services or an STI consultation and will complete a standardized closed format questionnaire upon completion of each health facility visit.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.036
GPT teacher head0.314
Teacher spread0.278 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

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