Accounting for health literacy and intervention preferences when reducing unhealthy snacking: protocol for an online randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Health literacy describes the cognitive and social skills that individuals use to access, understand and act on health information. Health literacy interventions typically take the 'universal precautions approach' where all consumers are presented with simplified materials. Although this approach can improve knowledge and comprehension, its impact on complex behaviours is less clear. Systematic reviews also suggest that health literacy interventions underuse volitional strategies (such as planning) that play an important role in behaviour change. A recent study found volitional strategies may need to be tailored to the participant's health literacy. The current study aims to replicate these findings in a sample of people who have diabetes and/or are overweight or obese as measured by body mass index, and to investigate the most effective method of allocating an action plan to a participant to reduce unhealthy snacking. METHODS AND ANALYSIS: We plan to recruit approximately 2400 participants at baseline. Participants will receive one of two alternative online action plans intended to reduce unhealthy snacking ('standard' action plan or 'literacy-sensitive' action plan). Participants will be randomised to a method of allocation to an action plan: (1) random allocation; (2) allocation by health literacy screening tool or (3) allocation by participant selection. Primary outcome is self-reported serves of unhealthy snacks during the previous month. Multiple linear regression will evaluate the impact of health literacy on intervention effectiveness. The analysis will also identify independent contributions of each action plan, method of allocation, health literacy and participant selections on unhealthy snacking at 4-week follow-up. ETHICS AND DISSEMINATION: This study was approved by the University of Sydney Human Research Ethics Committee (2017/793). Findings will be disseminated through peer-reviewed international journals, conferences and updates with collaborating public health bodies (Diabetes New South Wales (NSW) & Australian Capital Territory (ACT), and Western Sydney Local Health District). TRIAL REGISTRATION NUMBER: ACTRN12618001409268; Pre-results.
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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 | 0.060 | 0.071 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.128 | 0.022 |
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".