Current practice, perceived barriers and resource needs related to measurement of dietary intake, analysis and interpretation of data: A survey of Australian nutrition and dietetics practitioners and researchers
Bibliographic record
Abstract
AIM: To inform future training and professional development for individuals who measure, analyse and interpret dietary intake data. METHODS: A cross-sectional online survey was distributed via e-newsletter to members of Dietitians Australia, Dietitian Connection and Nutrition Society Australia. The survey included 37 questions on three key areas of practice: (a) methods used to assess dietary intake, (b) barriers faced when conducting dietary intake assessment and (c) resources needed to optimise collection, analysis and interpretation of dietary intake data. RESULTS: Of 173 responses, 103 respondents provided complete data over 2 weeks. Of these, 76% were APDs. The majority (90%) indicated that dietary assessment was important in their role. Respondents (63%) undertook dietary assessments to inform individual/patient care. When assessing intakes, the majority (79%) were interested in examining food/food group intakes. Paper based methods were most commonly used and diet histories, food frequency questionnaires and 24-hour recalls were the most frequently used methods. The biggest barrier identified to implementing dietary assessment methods into practice was participant burden. Over a third of respondents reported they had received specific training on selecting an appropriate dietary assessment method. The majority of respondents (83%) believed having access to a dietary assessment methods toolkit would be useful. CONCLUSION: Survey findings provide insight into the need for further capacity building strategies, including professional development to improve collection, analysis and interpretation of dietary intake for Australian nutritionists and dietitians. The creation of online resources could help overcome identified barriers and provide a link to best practice methodologies and contemporary tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".