Practitioner consenus on the determinants of capacity building practice in high-income countries
Bibliographic record
Abstract
Objective: To assess and develop consensus among experienced public health nutrition practitioners from high-income countries regarding conceptualisation of capacity building in practice, and to test the content validity of a previously published conceptual framework for capacity building in public health nutrition practice. Design: A Delphi study involving three iterations of email-delivered questionnaires testing a range of capacity determinants derived from the literature. Consensus was set at >50 % of panellists ranking items as ‘very important’ on a five-point Likert scale across three survey rounds. Setting: Public health nutrition practice in Australia, the UK, Canada and the USA. Subjects: Public health nutrition practitioners and academics. Result: A total of thirty expert panellists (68 % of an initial panel of forty-four participants) completed all three rounds of Delphi questionnaires. Consensus identified determinants of capacity building in practice including partnerships, resourcing, community development, leadership, workforce development, intelligence and quality of project management. Conclusions: The findings from the study suggest there is broad agreement among public health nutritionists from high-income countries about how they conceptualise capacity building in public health nutrition practice. This agreement suggests considerable content validity for a capacity building conceptual framework proposed by Baillie et al. (Public Health Nutr 12, 1031–1038). More research is needed to apply the conceptual framework to the implementation and evaluation of strategies that enhance the practice of capacity building approaches by public health nutrition professionals.
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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 | 0.052 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".