Validity of using mobile phone surveys to evaluate community health worker program in Mali
Bibliographic record
Abstract
BACKGROUND: The monitoring and evaluation of public health programs based on traditional face-to-face interviews in hard-to-reach and unstable regions present many challenges. Mobile phone-based methods are considered to be an effective alternative, but the validity of mobile phone-based data for assessing implementation strength has not been sufficiently studied yet. Nested within an evaluation project for an integrated community case management (iCCM) and family planning program in Mali, this study aimed to assess the validity of a mobile phone-based health provider survey to measure the implementation strength of this program. METHODS: From July to August 2018, a cross-sectional survey was conducted among the community health workers (ASCs) from six rural districts working with the iCCM and family planning program. ASCs were first reached to complete the mobile phone-based survey; within a week, ASCs were visited in their communities to complete the in-person survey. Both surveys used identical implementation strength tools to collect data on program activities related to iCCM and family planning. Sensitivity and specificity were calculated for each implementation strength indicator collected from the phone-based survey, with the in-person survey as the gold standard. A threshold of ≥ 80% for sensitivity and specificity was considered adequate for evaluation purposes. RESULTS: Of the 157 ASCs interviewed by mobile phone, 115 (73.2%) were reached in person. Most of the training (2/2 indicators), supervision (2/3), treatment/modern contraceptive supply (9/9), and reporting (3/3) indicators reached the 80% threshold for sensitivity, while only one supervision indicator and one supply indicator reached 80% for specificity. In contrast, most of the stock-out indicators (8/9) reached 80% for specificity, while only two indicators reached the threshold for sensitivity. CONCLUSIONS: The validity of mobile phone-based data was adequate for general training, supervision, and supply indicators for iCCM and family planning. With sufficient mobile phone coverage and reliable mobile network connection, mobile phone-based surveys are useful as an alternative for data collection to assess the implementation strength of general activities in hard-to-reach areas.
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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 | 0.040 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".