Exploring the Feasibility and Potential of Virtual Panels for Soliciting Feedback on Nutrition Education Materials: A Proof-of-Concept Study
Bibliographic record
Abstract
BACKGROUND: A changing and cluttered information landscape has put pressure on health organizations to produce consumer information materials that are not only factual but high quality and engaging to audiences. User-centered design methods can be useful in obtaining feedback from consumers; however, they are labor intensive and slow, which is not responsive to the fast-paced communication landscape influenced by social media. EatRight Ontario (ERO), a provincial nutrition and health support program of Dietitians of Canada, develops evidence-based resources for consumers and sought to increase user-centered design activities by exploring whether the standard approach to feedback could be replicated online. While online feedback has been used in marketing research, few examples are available in health promotion and public health to guide programming and policy. OBJECTIVE: This study compared a traditional in-person approach for recruitment and feedback using paper surveys with an Internet-based approach using Facebook as a recruitment tool and collecting user feedback via the Web. The purpose of the proof-of-concept study was to explore the feasibility of the approach and compare an online versus traditional approach in terms of recruitment issues and response. METHODS: An exploratory, two-group comparative trial was conducted using a convenience and purposive sampling. Participants reviewed a handout on healthy eating and then completed an 18-item survey with both forced-choice items and open-ended responses. One group viewed a hard-copy prototype and completed a paper survey and the other viewed a PDF prototype via Web links and completed a Web survey. The total days required to fulfill the sample for each group were used as the primary method of efficiency calculation. RESULTS: In total, 44 participants (22 per condition) completed the study, consisting of 42 women and 2 men over the age of 18. Few significant differences were detected between the groups. Statistically significant (P≤.05) differences were detected on four attitudinal variables related to the document reviewed and include perceived length of the document, perceived attractiveness, likelihood of contacting ERO for food and nutrition questions in the future, and likelihood of recommending ERO to a friend. In all cases, the responses were more favorable to the document or ERO with the online group. All other variables showed no difference between them. A content review of the qualitative feedback found relative consistency in word use and number of words used, indicating relative parity in the amount of data generated between conditions. The online condition achieved its sampling target in 9 days, while the in-person method took 79 days to achieve the target. CONCLUSIONS: An online process of recruitment through Facebook and solicitation of online feedback is a feasible model that yields comparable response levels to in-person methods for user feedback. The online approach appears to be a faster and less resource-intensive approach than traditional in-person methods for feedback generation.
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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 | 0.041 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".