Staff Recruitment Process for Public Health Intervention—A Case Study of the Stepping Stones Project
Bibliographic record
Abstract
The recruitment and selection process are two of the most important human resource functions having a great impact on the growth and success of an organization as compared to other tasks such as retention, onboarding, leadership development, and managing talent. Stepping Stones is a project for scaling the early childhood development of children younger than 5, implemented by Datta Meghe Institute of Medical Sciences in two districts of Central India through a generous seed grant from Grand Challenges, Canada. The project demanded a variety of human resources, including pre-primary teachers, pedagogy experts, social workers, and child psychologists. In particular, the field staff required essentially a blend of all these skills. A rigorous recruitment process was followed for this project. This article shares the authors’ strategy for identifying the productive workforce needed, specifically for a public health intervention project, and provides evidence of the effectiveness of their strategy, which may be useful for people working in the public health or development sector.
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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 | 0.048 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| 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, 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".