Consensus development on the essential competencies for Iranian public health nutritionists
Bibliographic record
Abstract
OBJECTIVE: To assess key experts' opinion regarding essential competencies required for effective public health nutrition practice within the health-care system of Iran. DESIGN: Qualitative study using the modified Delphi technique through an email-delivered questionnaire. SETTING: Iran. SUBJECTS: Fifty-five experts were contacted through email. The inclusion criterion for the study panel was being in a relevant senior-level position in nutrition science or public health nutrition in Iran. RESULTS: In the first round, forty-two out of fifty-five experts responded to the questionnaire (response rate=76 %). A sixty-five-item questionnaire was designed with nine competency areas, including 'nutrition science', 'planning and implementing nutritional interventions', 'health and nutrition services', 'advocacy and communication', 'assessment and analysis', 'evaluation', 'cultural, social and political aspects', 'using technology' and 'leadership and management'. All experts who had participated in the first round completed a modified version of the questionnaire with seventy-seven items in the second round. The experts scored 'nutrition science' as the most essential competency area, while more applied areas such as 'management and leadership' were less emphasized. In both rounds, the mean difference between the opinions of the necessity of each area was 5.6 %. CONCLUSIONS: The Iranian experts had general agreement on most of the core competency areas of public health nutritionists. The results indicated the need for capacity building and revisions to educational curricula for public health nutritionist programmes, with more emphasis on skill-based competency development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".