Using Evidence and Data to Design an Intervention in the Project Community Model for Fostering Health and Wellbeing Among Adolescent Mothers and Their Children
Bibliographic record
Abstract
In this paper, quantitative and qualitative measurements of maternal psychosocial wellbeing were utilized in three districts in Malawi that guided decision-making to increase the wellbeing of adolescent mothers and promote the healthy upbringing of their children. The 1-year design stage of the study relied on several sources of information: literature search, prior project implementation of similar projects, discussions with officials at the Malawi Department of Social Welfare, and observation visits in the targeted districts. The approaches for collecting data mentioned were triangulated for the development of a baseline survey. The baseline survey generated systematically collected data of the experiences and recalls as well as the missing data from the preliminary evaluation of the existing data. The baseline data gave the Young Women's Christian Association (YWCA) insight on the type of intervention required in order to give a greater and more holistic effect on the beneficiaries. We also discuss the lessons we learned as to whether the assumptions we had made at the onset were correct. If they were not correct, we explained the measures we took to correct the design or implementation of the project. Finally, the data provided benchmarks for project monitoring and evaluation.
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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 | 0.160 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".