The Online Health Information–Seeking Behaviors of People Who Have Experienced Stroke: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Stroke is a leading cause of death and disability worldwide. As health resources become digitized, it is important to understand how people who have experienced stroke engage with online health information. This understanding will aid in guiding the development and dissemination of online resources to support people after stroke. OBJECTIVE: This study aims to explore the online health information-seeking behaviors of people who have experienced stroke and any related barriers or navigational needs. METHODS: Purposeful sampling was used to recruit participants via email between March and November 2022. The sampling was done from an existing cohort of Australian stroke survivors who had previously participated in a randomized controlled trial of an online secondary prevention program. The cohort consisted of people with low levels of disability. Semistructured one-on-one interviews were conducted via phone or video calls. These calls were audio recorded and transcribed verbatim. The data were analyzed by 2 independent coders using a combined inductive-deductive approach. In the deductive analysis, responses were mapped to an online health information-seeking behavior framework. Inductive thematic analysis was used to analyze the remaining raw data that did not fit within the deductive theoretical framework. RESULTS: A sample of 15 relatively independent, high-functioning people who had experienced stroke from 4 Australian states, aged between 29 and 80 years, completed the interview. A broad range of online health information-seeking behaviors were identified, with most relating to participants wanting to be more informed about medical conditions and symptoms of their own or of a family member or a friend. Barriers included limited eHealth literacy and too much generalization of online information. Online resources were described to be more appealing and more accessible if they were high-quality, trustworthy, easy to use, and suggested by health care providers or trusted family members and friends. Across the interviews, there was an underlying theme of disconnection that appeared to impact not only the participants' online health information seeking, but their overall experience after stroke. These responses were grouped into 3 interrelated subthemes: disconnection from conventional stroke narratives and resources, disconnection from the continuing significance of stroke, and disconnection from long-term supports. CONCLUSIONS: People who have experienced stroke actively engage with the internet to search for health information with varying levels of confidence. The underlying theme of disconnection identified in the interviews highlights the need for a more comprehensive and sustained framework for support after stroke beyond the initial recovery phase. Future research should explore the development of tailored and relatable internet-based resources, improved communication and education about the diversity of stroke experiences and ongoing risks, and increased opportunities for long-term support.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".