Impact Evaluation of the Kenya Frontline Field Epidemiology Training Program: Repeated-Measures Study
Bibliographic record
Abstract
BACKGROUND: In 2014, Kenya's Field Epidemiology and Laboratory Training Program (FELTP) initiated a 3-month field-based frontline training, Field Epidemiology Training Program (FETP-F), for local public health workers. OBJECTIVE: This study aimed to measure the effect of FETP-F on participant workplace practices regarding quality and consistency of public health data, critical interaction with public health data, and improvements in on-time reporting (OTR). METHODS: Between February and April 2017, FELTP conducted a mixed methods evaluation via online survey to examine outcomes achieved among all 215 graduates from 2014 and 2015. Data quality assessment (DQA) and data consistency assessment (DCA) scores, OTR percentages, and ratings of the training experience were the quantitative measures tracked from baseline and then at 6-month intervals up to 18 months postcompletion of the training. The qualitative component consisted of semistructured face-to-face interviews and observations. Quantitative data were analyzed using descriptive statistics and one-way analysis of variance (ANOVA). Qualitative data were transcribed and analyzed to identify key themes and dimensions. RESULTS: In total, 103 (47%) graduates responded to the survey. Quantitative analyses showed that the training significantly increased the mean DQA and OTR scores but there was a nonsignificant increase in mean DCA scores. Qualitative analyses found that 68% of respondents acquired new skills, 83% applied those skills to their day-to-day work, and 91% improved work methods. CONCLUSIONS: FETP-F improved overall data quality and OTR at the agency level but had minimal impact on data consistency between local, county, and national public health agencies. Participants reported that they acquired practical skills that improved data collation and analysis and OTR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".