A practical, cost-effective method for recruiting people into healthy eating behavior programs.
Bibliographic record
Abstract
INTRODUCTION: The population impact of programs designed to develop healthy eating behaviors is limited by the number of people who use them. Most public health providers and researchers rely on purchased mass media, which can be expensive, on public service announcements, or clinic-based recruitment, which can have limited reach. Few studies offer assistance for selecting high-outreach and low-cost strategies to promote healthy eating programs. The purpose of this study was 1) to determine whether classified newspaper advertising is an effective and efficient method of recruiting participants into a healthy eating program and 2) to determine whether segmenting messages by transtheoretical stage of change would help engage individuals at all levels of motivation to change their eating behavior. METHODS: For 5 days in 1997, three advertisements corresponding to different stages of change were placed in a Canadian newspaper with a daily circulation of 75,000. RESULTS: There were 282 eligible people who responded to newspaper advertisements, and the cost was Can $1.11 (U.S. $0.72) per recruit. This cost compares favorably with the cost efficiency of mass media, direct mail, and other common promotional methods. Message type was correlated with respondent's stage of change, and this correlation suggested that attempts to send different messages to different audience segments were successful. CONCLUSION: Classified advertisements appear to be a highly cost-efficient method for recruiting a diverse range of participants into healthy eating programs and research about healthy eating.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.027 |
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".