Current Approaches to Evaluating Energy Requirements and Intake among Practicing Registered Dietitians
Bibliographic record
Abstract
Abstract Background/Objectives Assessment of energy requirements and intake is central to the nutrition care process, yet current practices among registered dietitians (RDs) are not well characterized. This study examined how RDs assess energy requirements and intake, including perceived accuracy and resources, and differences by setting and experience. Methods A cross-sectional bilingual online survey was administered to RDs in Canada. The survey collected information on practice setting and experience, access to variables influencing energy requirements/intake, tool use, and opinions on accuracy and resource needs. Descriptive statistics and comparisons were made by practice setting (clinical, community, other) and years in practice (<5, 5–10, >10 years). Results 212 RDs completed the survey (62% clinical, 16% community, 22% other settings; 36% <5 years, 23% 5–10 years, 42% >10 years of practice). Participants rated importance of assessing energy requirements and energy intake as moderately high (6.8□±□2.2, 7.1□±□2.3 out of 10, respectively) and had regular access to variables needed to calculate energy requirements and intake (e.g., age, sex, weight, disease), although access to body composition, sleep, and stress was limited. Commonly-used tools included body weight-based equations and 24-hour recalls. Confidence was highest for delivering interventions and lowest for assessing intake (p < 0.001), especially among less experienced RDs (p = 0.002). Most respondents expressed interest in improved tools for assessing energy requirements (76%) and intake (74%). Conclusion Current RD practices vary, and access to key data is limited, underscoring the need for validated, accessible tools and training to support accurate energy assessment in dietetic care.
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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 | 0.033 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".