HOME ENVIRONMENT PREDICTS ADOLESCENT INTENTION TO EAT ANEMIA PREVENTION FOODS IN INDONESIA
Bibliographic record
Abstract
Rationale and Objectives: The primary aim of this study was to develop clarity around the use of the term capacity building within the field of public health nutrition (PHN). The objectives of this study were to assess agreement between how people understand the term and to build a common understanding of capacity building relevant to PHN practice. Materials and Methods: The Delphi technique was selected for this study in order to determine aspects of capacity building in PHN practice. The study consisted of three rounds of questionnaires whereby participants were asked to rank the importance of different attributes of capacity building on a five point Likert scale. The expert panel was made up of PHN leaders, skilled practitioners and researchers who were recruited from existing PHN networks and interest groups. Approximately 300 nutritionists from Australia, the UK, Canada and the USA were invited to participate in the study. Forty-five experts agreed to initially take part in the study. Forty-five experts agreed to initially take part in the study, and of these 34 completed all three rounds of questionnaires. Results and Findings: Consensus was defined as a reduction in variation between responses from one round to the next. The determinants of capacity building that obtained the highest level of expert agreement include: partnerships, human resources, community participation, leadership, workforce competencies and preparedness, needs assessment, problem solving ability, and knowledge transfer. Conclusion: Increasing awareness and understanding of capacity building enables better application of its principles in PHN practice. The results of this consensys study will also be used to inform the development of a checklist for the planning and evaluating capacity building strategies within PHN practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".