Use of Mobile Device Applications In Canadian Dietetic Practice
Bibliographic record
Abstract
PURPOSE: A cross-sectional web-based survey of dietitians was used to explore topics related to mobile devices and their applications (apps) in Canadian dietetic practice. METHODS: A survey was drafted, posted on SurveyMonkey, and pretested with dietitians and dietetic interns. Dietitians of Canada (DC), a supporter of this work, promoted the survey to members through its monthly electronic newsletters from January 2012 to April 2012. RESULTS: Of 139 dietitians who answered some survey questions, 118 finished the survey; this represents a response rate of approximately 3%. Overall, 57.3% of respondents reported app use in practice, and 54.2% had a client ask about or use a nutrition/food app. About 40.5% of respondents had recommended nutrition/food apps to clients. Respondents were enthusiastic about apps, but many described challenges with use. From the survey data, three themes emerged that can affect dietitians' use of apps and whether they recommend apps to clients: mobile device and app factors (access to information/tools, content quality, usability, accessibility/compatibility, and cost), personal factors (knowledge, interest, suitability, and willingness/ability to pay), and workplace factors. CONCLUSIONS: Apps are now infiltrating dietetic practice. Several factors can affect dietitians' use of apps and whether they recommend them to clients. These findings will help guide future development and use of apps in 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".