The role of motivational design in health education: an examination of computer-based education on women, smoking and health
Bibliographic record
Abstract
This study examined the effects of motivational design on affective and cognitive outcomes of computer-based education on smoking and health among women. A program developed using the principles of Keller's approach to motivational design was compared with a generic web site on smoking created by the Canadian Cancer society. In addition, it used Prochaska's Stages of Change model to examine the effects of frames of reference with respect to smoking cessation. A sample of 40 adult women who currently smoke or quit within the last six months volunteered to participate in an evaluation of two programs on women, smoking and health: (a) Breath of Fresh Air—a program designed specifically for women and developed with Keller's principles of motivational design as its theoretical underpinning; (b) Canadian Cancer Society web site—a web-site on smoking and health designed for a generic audience. The participants in the Breath of Fresh Air were substantially more positive in their response to the software on all measures of motivational outcomes compared with the users of the Cancer Society web site. Moreover, these participants were also more likely to indicate that they were more motivated to quit smoking after using the software than their counterparts in the control group. However, motivation to quit smoking was also clearly affected by the participant's stage of change, such that pre-contemplators were less likely to say that they were more motivated to quit independent of which study group they were in. This study provided clear evidence that instructional design and frames of reference can both affect outcomes to computer-based health education. Other factors like education, computer experience, and age tended to have only modest effects on a limited set of outcomes. The implication of this work is that efforts to improve population health through computer based education must take into account the characteristics of the target audience in the design of those materials and that strategies to enhance motivation are essential ingredients to the success of those interventions.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".