Evaluating primary care programmes: a problem-solving cycle with literature review on programme evaluation for cervical cancer screening at a community health centre, Jakarta, Indonesia
Bibliographic record
Abstract
Search, G -Funds CollectionBackground.Cervical cancer (CC) poses a significant burden on various aspects of public health, including overall wellbeing, social dynamics and economic factors.Objectives.This report aimed to evaluate the effectiveness of a primary care programme focused on CC screening through the Visual Inspection with Acetic Acid (VIA) test at a community health centre (CHC) in urban South Jakarta, Indonesia.Material and methods.We used a problem-solving cycle (PSC) approach to evaluate the programme's implementation and outcomes.This evaluation process included problem identification, situation analysis, root-cause determination using the Ishikawa diagram, prioritisation of problem-solving strategies based on the urgency, seriousness and growth (USG) and importance, technical feasibility and resource availability (ITR) matrix, as well as proposing recommendations for improvement using the magnitude, importance, vulnerability and cost (MIV/C) matrix.Results. the low VIA test coverage at our CHC (13.39% in 2020) highlights the need for targeted interventions to increase participation.Inefficient implementation of the programme stems from various reasons (input, process and environmental factors).The solutions target root causes such as material, actuation and community response to improve CC screening.Inadequate knowledge, limited healthcare accessibility and socio-economic disparities hinder programme success.Despite these challenges, programme evaluation can enhance the level of community health, improve the quality of life, increase early CC case detection and reduce morbidity and mortality.Conclusions.This study offers valuable insights and guidance for healthcare professionals in improving primary care programmes for CC screening.Future efforts should focus on addressing barriers and implementing targeted strategies to enhance programme effectiveness and reach.
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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 | 0.084 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".