Engagement, Innovation, and Impact in a Dietitian Contact Centre: The EatRight Ontario Experience
Bibliographic record
Abstract
Purpose: EatRight Ontario (ERO), a multi-modal dietitian service (phone, email, web), provided the public and health intermediaries with healthy eating advice, professional support, and health promotion tools from 2007 to 2018. An evaluation of ERO was conducted to assess the impact of the model on knowledge, attitudes, and behaviour for consumers, utilization, and support levels and satisfaction provided to health intermediaries. Methods: Consumer clients were sent a survey 1–4 weeks after using the ERO service to capture self-reported dietary changes, intentions, nutritional knowledge, and satisfaction. Health intermediaries were recruited through an electronic ERO newsletter and asked about how ERO supported their practice. Results: Of the 867 consumer respondents, 92% had either made a change or indicated that information from ERO confirmed their present behaviour, and 96% indicated they would recommend the services to others. Of the 337 health intermediaries who responded 71% indicated that ERO provided services they could not deliver. Conclusions: ERO’s multi-modal dietitian contact centre provides a model for implementing successful remote service access for consumers and professionals to support healthy eating across diverse demographics and geographies, including those in geographically underserved areas.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".