Best Practices in Advancing Family Well-Being in Asia: A Multimethod Qualitative Study
Bibliographic record
Abstract
Family has been recognized as the basic unit of society. Strengthening family functioning and enhancing family well-being through promoting family-oriented policies that based on evidence that demonstrates the effectiveness and practicalities of interventions are important. However, the planning and evaluation of existing programs is not universally agreed upon due to a lack of guiding evaluation framework and different cultural contexts. This study aims to identify best practices and consolidate social impacts of programs that support family well-being in the Asian Region, data was drawn on the Wofoo Asian Award for Advancing Family Well-Being Project (3A Project), initiated by the Consortium of Institutes on Family in the Asian Region (CIFA). A multimethod qualitative study was conducted, including a review of documents on the 3A Project, documents submitted by a total of forty awarded projects, and four in-depth interviews with team leaders of awarded projects. All data were analyzed in parallel and triangulated in the interpretation of findings. Informed by the logic model of program development and evaluation, this study discovered six overarching best practices ― PIE-ISI ― were identified: (i) Project rationales; (ii) Implementation; (iii) Evaluation; (iv) Innovation; (v) Sustainability and replicability; and (vi) Institutional synergy.
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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 | 0.036 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".