Community health workers at the dawn of a new era: 10. Programme performance and its assessment
Bibliographic record
Abstract
BACKGROUND: While the evidence supporting the effectiveness of community health worker (CHW) programmes is substantial, there is also considerable evidence that many of these programmes have notable weaknesses that need to be addressed in order for them to reach their full potential. Thus, considerations about CHW programme performance and its assessment must be taken into account as the importance of these programmes is becoming more widely appreciated. In this paper, the tenth in our 11-paper series, "Community health workers at the dawn of a new era", we address CHW programme performance and how it is assessed from a systems perspective. METHODS: The paper builds on the 2014 CHW Reference Guide, a compendium of case studies of 29 national CHW programmes, the 2018 WHO guideline on health policy and system support to optimize CHW programmes, and scientific studies on CHW programme performance published in the past 5 years. RESULTS: The paper provides an overview of existing frameworks that are useful for assessing the performance of CHW programmes, with a specific focus on how individual CHW performance and community-level outcomes can be measured. The paper also reviews approaches that have been taken to assess CHW programme performance, from programme monitoring using the routine health information system to national assessments using quantitative and/or qualitative study designs and assessment checklists. The paper also discusses contextual factors that influence CHW programme performance, and reflects upon gaps and needs for the future with regard to assessment of CHW programme performance. CONCLUSION: Assessments of CHW programme performance can have various approaches and foci according to the programme and its context. Given the fact that CHW programmes are complex entities and part of health systems, their assessment ideally needs to be based on data derived from a mix of reliable sources. Assessments should be focused not only on effectiveness (what works) but also on contextual factors and enablers (how, for whom, under what circumstances). Investment in performance assessment is instrumental for continually innovating, upgrading, and improving CHW programmes at scale. Now is the time for new efforts in implementation research for strengthening CHW programming.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.052 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".