Defining Research Priorities for Nutrition and Mental Health: Insights from Dietetics Practice
Bibliographic record
Abstract
In 2014, a national initiative aimed at defining a research agenda for nutrition and mental health among diverse stakeholders was completed and included insights from more than 300 registered dietitians. This study explores the data from dietitians based on their years of practice, mental health experiences, and community of practice in relationship to identified mental health and nutrition research priorities. Analysis of numerical data (n = 299) and content analysis of open-ended responses (n = 269) revealed that respondents desired research for specific mental health conditions (MHCs), emotional eating, food addiction, populations with special needs, and people encountering major life transitions (e.g., recovery from abuse, refugees). Findings from the quantitative and textual data suggested that dietitians want research aimed at addressing the concerns of those in the community, fostering consumer nutrition knowledge and skill acquisition, and developing services that will impact quality of life. Subgroup analysis indicated that dietitians: (i) in early years of practice want information about specific MHCs; (ii) living in smaller towns and rural areas want data about the cost benefits of dietetics practice in mental health; and (iii) who also had additional stakeholder roles (e.g., service provider) selected priorities that address gaps in mental health services. This study highlights opportunities to tailor nutrition and mental health research that advance dietetics 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 | 0.037 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".