The website-based eaTracker<sup>®</sup>‘My Goals’ feature: a qualitative evaluation
Bibliographic record
Abstract
OBJECTIVE: In 2011, Dietitians of Canada added 'My Goals' to its website-based nutrition/activity tracking program (eaTracker®, http://www.eaTracker.ca/); this feature allows users to choose 'ready-made' or 'write-your-own' goals and to self-report progress. The purpose of the present study was to document experiences and perceptions of goal setting and My Goals, and report users' feedback on what is needed in future website-based goal setting/tracking tools. DESIGN: One-on-one semi-structured interviews were conducted with (i) My Goals users and (ii) dietitians providing a public information support service, EatRight Ontario (ERO). SETTING: My Goals users from Ontario and Alberta, Canada were recruited via an eaTracker website pop-up box; ERO dietitians working in Ontario, Canada were recruited via ERO. SUBJECTS: My Goals users (n 23; age 19-70 years; 91 % female; n 5 from Alberta/n 18 from Ontario) and ERO dietitians (n 5). RESULTS: Dietitians and users felt goal setting for nutrition (and activity) behaviour change was both a beneficial and a challenging process. Dietitians were concerned about users setting poor-quality goals and users felt it was difficult to stick to their goals. Both users and dietitians were enthusiastic about the My Goals concept, but felt the current feature had limitations that affected use. Dietitians and users provided suggestions to improve My Goals (e.g. more prominent presence of My Goals in eaTracker; assistance with goal setting; automated personalized feedback). CONCLUSIONS: Dietitians and users shared similar perspectives on the My Goals feature and both felt goal use was challenging. Several suggestions were provided to enhance My Goals that are relevant to website-based goal setting/tracking tool design in general.
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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 | 0.049 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".