Guatemala (2013): Assessment of Quality of Service Provision to Most-at-Risk Populations by Private Sector Providers in Central America. Round 1.
Bibliographic record
Abstract
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".