Uptake of appointment spacing model of care and associated factors among stable adult HIV clients on antiretroviral treatment Northwest Ethiopia
Bibliographic record
Abstract
INTRODUCTION: Ethiopia launched an Appointment Spacing Model in 2017, which involved a six-month clinical visit and medication refill cycle. This study aimed to assess the uptake of the Appointment Spacing Model of care and associated factors among stable adult HIV clients on ART in Ethiopia. METHODS: A cross-sectional study was conducted from October 3 to November 30, 2020 among 415 stable adult ART clients. EpiData version 4.2 was used for data entry and SPSS version 25 was used for cleaning and analysis. A multivariable logistic regression model was fitted to identify associated factors, with CI at 95% with AOR being reported to show the strength of association. RESULTS: The uptake of the appointment spacing model was 50.1%. Residence [AOR: 2.33 (95% CI: 1.27, 4.26)], monthly income [AOR: 2.65 (95% CI: 1.13, 6.24)], social support [AOR: 2.21 (95% CI: 1.03, 4.71)], duration on ART [AOR: 2.41 (95% CI: 1.48, 3.92)], baseline regimen change [AOR: 2.20 (95% CI: 1.02, 4.78)], viral load [AOR: 2.80 (95% CI: 1.06, 7.35)], and alcohol abstinence [AOR: 2.02 (95% CI: 1.21, 3.37)] were statistically significant. CONCLUSIONS: The uptake of the ASM was low. Behavioral change communication, engaging income-generating activities, and facility-level service providers' training may improve the uptake.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".