The impact of validated, online health education resources on patient and community members’ satisfaction and health behaviour
Bibliographic record
Abstract
Objective: While access to health education information has become easier, the quality of information retrieved from the Internet varies considerably. In response to the need for accessible, quality health information that is tailored to meet individual patient needs, a patient education website, called PEPTalk, was developed. The site houses text and video material that has been validated by practicing clinicians. A study was conducted to examine patient and community members’ satisfaction with PEPTalk and the impact of the health education materials on their health behaviour. Community staff and health providers’ experiences with the new technology were also examined. Design and method: A descriptive study using surveys and interviews was conducted with 57 patients, community participants and clinicians living in a large Canadian city and First Nations communities in Northern Ontario. Results: Participants’ PEPTalk Satisfaction scores ranged from moderately to highly satisfied. Participants found the information presented on PEPTalk useful and relevant, had improved their knowledge of health, and in most cases, altered health behaviour. Clinicians and community staff who referred participants to the PEPTalk website reported that the site provided reliable, evidence-based information that they were comfortable sharing with their patients and community members. Conclusion: There is an emerging role for tools that provide tailored health education. The health provider’s role regarding interpretation, discussion and follow-up remains essential, and tools such as PEPTalk need to be part of an overall health education strategy.
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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 | 0.004 | 0.023 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".