Program design features that can improve participation in health education interventions
Bibliographic record
Abstract
BACKGROUND: Although there have been reported benefits of health education interventions across various health issues, the key to program effectiveness is participation and retention. Unfortunately, not everyone is willing to participate in health interventions upon invitation. In fact, health education interventions are vulnerable to low participation rates. The objective of this study was to identify design features that may increase participation in health education interventions and evaluation surveys, and to maximize recruitment and retention efforts in a general ambulatory population. METHODS: A cross-sectional questionnaire was administered to 175 individuals in waiting rooms of two hospitals diagnostic centres in Toronto, Canada. Subjects were asked about their willingness to participate, in principle, and the extent of their participation (frequency and duration) in health education interventions under various settings and in intervention evaluation surveys using various survey methods. RESULTS: The majority of respondents preferred to participate in one 30-60 minutes education intervention session a year, in hospital either with a group or one-on-one with an educator. Also, the majority of respondents preferred to spend 20-30 minutes each time, completing one to two evaluation surveys per year in hospital or by mail. CONCLUSION: When designing interventions and their evaluation surveys, it is important to consider the preferences for setting, length of participation and survey method of your target population, in order to maximize recruitment and retention efforts. Study respondents preferred short and convenient health education interventions and surveys. Therefore, brevity, convenience and choice appear to be important when designing education interventions and evaluation surveys from the perspective of our target population.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".