A Weekly, Evidence-Based Health Letter for Caregivers (90Second Caregiver): Usability Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Informal caregivers are family members or close friends who provide unpaid help to individuals with acute or chronic health conditions so that they can manage daily life tasks. The greatest source of health information is the internet for meeting the needs of caregivers. However, information on the internet may not be scientifically valid, it may be written in language that is difficult to read, and is often in very large doses. 90Second Caregiver is a health letter whose aim is to disseminate knowledge to caregivers in a user-friendly, weekly format, in order to improve their wellbeing. OBJECTIVE The main objective was to test a sample of 90Second Caregiver health letters in order to assess their usability and to optimize the design and content of the health letters. METHODS Usability research themes were assessed using semi-structured phone interviews, incorporating the Think Aloud method with retrospective questioning. RESULTS Usability was assessed in the context of five main themes: understandability and learnability, completeness, relevance, and quality and credibility of the health letter content, as well as design and format. Caregivers generally provided positive feedback regarding the usability of the letters. The usability feedback was used to refine 90Second Caregiver in order to improve the design and content of the series. Based on the results of this study, it may be of maximum benefit to target the series towards individuals who are new to caregiving or part-time caregivers, given that these caregivers of the sample found the letters more useful and relevant and had the most positive usability experiences. CONCLUSIONS The findings assisted in the improvement of the 90Second Caregiver template, which will be used to create future health letters and refine the letters that have already been created. The findings have implications for who the 90Second Caregiver series should be targeting (ie, newer or part-time caregivers) in order to be maximally impactful in improving mental health and wellbeing-related outcomes for caregivers, such as self-efficacy and caregiving knowledge. The results of this study may be generalizable to the examination of other electronic health information formats, making them valuable to future researchers testing the usability of health information products. In addition, the methods used in this study are useful for usability hypothesis generation. Lastly, our 90Second delivery approach can generate information useful for a set of similar products (eg, weekly health letters targeted towards other conditions/populations).
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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 | 0.033 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".