Recruitment of Cohort Number 20 of Field Epidemiology Training Program — Lessons Learned — Egypt, 2017
Bibliographic record
Abstract
Background: The Field Epidemiology Training Program (FETP) had been established in Egypt since 1993. It was the second in the Eastern Mediterranean region. Up to this year, 18 cohorts were graduated from FETP. Approximately 80% of graduates work in the Egyptian ministry of health and population (MOHP) and many fill leadership positions. Others provide essential epidemiological services abroad and to neighboring countries. Objective: We aim to describe and evaluate recruitment process for new FETP residents. Methods: An announcement for recruitment of a new cohort was published on newsletter and on FETP Egypt Facebook page on September 31 for two weeks. Inclusion criteria listed on the announcement based on MoHP requirements. Interested candidates were encouraged to fill an application on FETP Egypt website. The application consists of open ended as well as closed questions. Data were extracted, edit and cleaned using Microsoft Excel. One hundred applicants were short listed and invited by phone call to a Face-to-Face interview. Candidate evaluation was web based, it included measurement of skills, qualifications, and experience using Likert scale. Results: Out 364 responses, 39/346 (11.3%) were replicate applications making 307 total applicants. A total of 269/307 (87.6%) applicants were eligible for FETP requirements. Proportion of females was 176/269 (65.4%). Out of 269 candidates; pharmacists, physicians, dentists and veterinarians were 170 (63.2%), 73 (27.1%), 20 (7.4%), 6 (2.2%) respectively. Out of 100 shortlisted applicants; 87 (87%) responded to the call for the interview. A total of 75 interviewed candidates were placed in various departments of preventive sector; 27 (36.0%) at central level and 48 (64.0%) at directorate level representing 19 governorates. Seven candidates (8.0%) were out of the preventive sector and five (5.7%) were veterinarians serving ministry of agriculture. Conclusions: The online applications made recruitment process ran smoothly; it was much easier and less time consuming. However, open ended questions could be closed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".