Use of electronic dietary assessment tools in primary care: an interdisciplinary perspective
Bibliographic record
Abstract
BACKGROUND: Dietary assessment can be challenging for many reasons, including the wide variety of foods, eating patterns and nutrients to be considered. In team-based primary care practice, various disciplines may be involved in assessing diet. Electronic-based dietary assessment (e-DA) instruments available now through mobile apps or websites can potentially facilitate dietary assessment. Providers views of facilitators and barriers related to e-DA instruments and their recommendations for improvement can inform the further development of these tools. The objective of this study was to explore provider perspectives on e-DA tools in mobile apps and websites. METHODS: The exploratory sequential mixed methods design included interdisciplinary focus groups followed by a web-based survey sent to Family Health Teams throughout Ontario, Canada. Descriptive and bivariate analyses were completed. Focus group transcripts contributed to web-survey content, while interpretive themes added depth and context. RESULTS: 11 focus groups with 50 providers revealed varying perspectives on the use of e-DA for: 1) improving patients' eating habits; 2) improving the quality of dietary assessment; and, 3) integrating e-DA into the care process. In the web-survey 191 respondents from nine disciplines in 73 FHTs completed the survey. Dietitians reported greater use of e-DA than other providers (63% vs.19%; p = .000) respectively. There was strong interest among disciplines in the use of e-DA tools for the management of obesity, diabetes and heart disease, especially for patient self-monitoring. Barriers identified were: patients' lack of comfort with using technology, misinterpretation of e-DA results by patients, time and education for providers to interpret results, and time for providers to offer counselling. CONCLUSIONS: e-DA tools in mobile apps and websites may improve dietary counselling over time. Addressing the identified facilitators and barriers can potentially promote the uptake of e-DA into clinical 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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".