A Qualitative Evaluation of Contact Centre Dietitian Support and Electronic Motivational Messaging for eaTracker My Goals Users
Bibliographic record
Abstract
PURPOSE: To conduct a qualitative evaluation of adjunct supports (brief motivational messaging regarding goals delivered by email/website, contact centre dietitian assistance) offered by EatRight Ontario (ERO) for users of a website-based nutrition/activity goal setting/tracking feature (eaTracker "My Goals"). METHODS: One-on-one semi-structured interviews were conducted with My Goals users in Ontario (n = 18) and Alberta (n = 5) recruited via the eaTracker website and ERO contact centre dietitians (n = 5). Interview transcripts were analyzed using content analysis. RESULTS: Participants had mixed experiences and perspectives with ERO motivational messaging. Messages targeted towards specific goals (e.g., tips, recipes) were generally well-liked, and generic messages (e.g., eaTracker login reminders) were less useful. No interviewed users had contacted ERO dietitians regarding goals, and dietitians reported encountering few callers asking for assistance while using My Goals. Limited user knowledge was one explanation for this finding. Participants provided suggestions to enhance these supports. CONCLUSION: Electronic motivational messaging and contact centre dietitian assistance have the potential to support achievement of goals set with website-based features. When considering using electronic messaging, researchers and practitioners should consider message content and delivery tailoring. Marketing that focuses on how contact centre dietitians can assist website users with their goals is needed when services are used in naturalistic settings.
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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 | 0.048 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".