Would People Pay for Text Messaging Health Reminders?
Bibliographic record
Abstract
The aim of this study is to determine the time and financial limitations that people would accept for using a telehealth service consisting of wireless text messaging reminders to improve adherence to a recommended healthy regimen. An empirical study based on a 1-month trial of a prototype system that studied adherence to a specified healthy behaviour was conducted. Fifty-one participants received daily cell phone text messaging reminders on taking one vitamin C pill daily for preventive reasons. At the end of the trial they answered a survey regarding their willingness to pay for and to stay with such a service, if offered. If usage were free, only 45% of the participants would continue to use it for a long indefinite period of time. If the usage were for a fee, 29% of the participants would use the service just a few weeks; 28% would use it an indefinite period of time if they could see its usefulness and if the cost were reasonable. The median amount indicated by the participants as a reasonable monthly fee for such a service was $5. Although the study did not evaluate perceived usefulness to use the telehealth service explicitly, a benefit perception proved to condition participant willingness to use the service and to pay for it, if necessary. If people perceive usefulness, they want to use the service, even for a fee. A free service would not be used if it is not perceived as beneficial.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".