9. Creating Effective Health Promotion Messages: Using Eye Tracking Technology Coupled with Masked Recall to Determine the Effectiveness of Loss and Gain Framed Osteoporosis Advertisements
Bibliographic record
Abstract
Osteoporosis is a debilitating disease which afflicts over 25 percent of Canadian women over the age of 50, and can lead to serious fractures.(Osteoporosis Canada, 2009) What is the most startling about this disease is that osteoporosis is largely preventable by taking calcium and vitamin D supplements and enjoying a healthy, active lifestyle. The challenge then, is to figure out ways to effectively communicate prevention related health messages. By framing messages either by naming or showing the consequences (loss framed) naming or showing the benefits (gain framed) or simply stating the facts (neutral framed), message framing can be a persuasive communication tool to affect changes in behaviour (Pelletier & Sharp, 2008) Using eye tracking technology‐ which is a device used to measure a participant’s attention to advertisements ‐ data will be collected to monitor the number of eye fixations, and the dwell time, or total amount of time looking at a particular advertisement. This information will be used to determine what types of messages (loss, gain, or neutral framed) garner more audience attention. The eye tracking data will be coupled with an exercise after the eye tracking experiment where participants are asked to recall what was written in the health advertisement messages. This exercise will provide information on whether loss, gain or neutral framed messages were more effective for audience recollection, which is significant because the messages recalled more easily are more likely to change perceptions, attitudes and behaviours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.011 | 0.003 |
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".