Using web based software solution to maintain resident nutrition profiles and offer table side ordering in long term care to enhance resident dining experience and create efficiency.
Bibliographic record
Abstract
PurposeTo identify current mobile health (mHealth) promoting technologies and cultivate a summary of recommendations for the development of a mobile application intended to support individuals with healthy eating behavior change within Newfoundland and Labrador (NL).Process or summary of contentA selection of mHealth apps that emphasize weight and calorie counting are presently available. Research is limited on their accuracy, success, and long-term usefulness. Furthermore, these apps have minimal focus on healthy eating and behaviour change. Individuals are more likely to be successful making positive dietary changes when they can identify their goals via a client-centered approach. mHealth can motivate and support clients, resulting in improved self-efficacy with achieving personalized goals.Systematic approach An environmental scan and analysis of existing technologies (i.e. mobile apps, websites, etc.) that focus on health-promoting behaviors were searched using CADTH, PubMed and Google Scholar. A literature review was conducted on motivation and other factors influencing behaviour change. Consultation with national, provincial, and local stakeholders provided qualitative data pertaining to the feasibility and level of interest in the development and implementation of this type of technology.ConclusionsAt present, there are limited applications available that meet the needs of clients and dietitians. Introducing a mHealth application into dietetic practice in NL is a cost-effective method to enhance dietetic services province-wide. Recommendation It is recommended that the government of NL invest in the development of mHealth application tailored to support the practice of dietitians, their clientele, and individuals who are working to improve their eating behaviour. Significance to the field of dieteticsThe findings have been translated into recommendations for the development and implementation of a mHealth application targeted at improving healthy eating within NL. This technology will foster a client-centered approach to behaviour change related to healthy eating.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".