Father Involvement in Early Childhood Care: Insights From a MEL System in a Behavior Change Intervention Among Rural Indian Parents
Bibliographic record
Abstract
Introduction: Fathers’ involvement in care and early initiation of cognitive development activities have a positive impact on a child’s social-behavioral, cognitive-academic and emotional-psychological development. This research study, conducted in Tamil Nadu in south India (2017-19), employed a Cluster Randomized Trial to test the impact of techno-social innovations in improving the involvement of fathers in child-care on child development outcomes. Qualitative studies were used to inform the trial and provide insights into pathways of change. Objective: This paper discusses the design, implementation and results of the study through the monitoring, evaluation and learning (MEL) framework to provide an understanding of the perceptions among parents and service providers surrounding early child development, the adaptations and learnings through the intervention period, and changes that were brought about through the intervention. Methods: The study was at a Proof of Concept stage, and the primary learning objective was to keep the learning process going through the period of the study, as well as obtain evidence to inform future model development. The measurement for change process in the study occurred in three distinct yet interconnected stages. In the first stage, the program was planned, and the design was refined for both the implementation and evaluation of the project. The next stage was the actual implementation: with a learning loop during the execution of the main intervention. The third stage was intended to reflect on the adaptations and pathways to change through the project period and collate evidence for model refinement. Results & Discussion: The data collected from the formative research was used to design, develop and implement the intervention. Lessons in coordination with the government program not only brought policy visibility, access to secondary data, and enabled field research, but also provided access to a workforce with immense field knowledge and presence in the rural underserved population. In order to continuously inform the implementation process of the intervention, the feedback loops allowed for adaptions to be made at each stage. The findings provide insights for programming early childhood development interventions, especially interventions regarding improving father’s involvement in child-care, and ways to leverage evidence in these interventions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".