The Motivations of Citizens to Attend an eHealth Course in the Public Library: Qualitative Interview Study
Bibliographic record
Abstract
Background: There is worldwide recognition of the potential increase of digital health inequity due to the increased digitalization of health care systems. Digital health skill development may prevent disparities in eHealth access and use. In the Dutch context, the public library has started to facilitate support in digital health skill development by offering public eHealth courses. Understanding the motivations of people to seek support may help to further develop this type of public service. Objective: This is a qualitative study on the motivations of citizens participating in an eHealth course offered by public libraries. The study aimed to explore why citizens were motivated to seek nonformal support for eHealth use. Methods: A total of 20 semistructured interviews with participants who participated in an eHealth course were conducted in 7 public libraries across the Netherlands. The interviews were conducted between April and June 2022. Purposive sampling took place in the public library during the eHealth course. The interviews covered participants' motivations, attitudes, and experiences with eHealth use and their motivations to seek help with eHealth use. Interviews were audio-recorded and transcribed. Themes were identified via a comprehensive thematic data analysis. Results: The participants were 51 to 82 years of age (average 73.5, SD 6.6 y) and 14 (70%) participants were female. Three motivational themes were identified: (1) adapting to an increasingly digital society, (2) sense of urgency facilitated by prior experience in health care, and (3) a need for self-reliance and autonomy. Additionally, participants expressed a general desire for social contact and lifelong learning. A lack of adequate informal support by friends and family for digital skills and positive experiences with formal support from public libraries stimulated the participants to seek formal support for eHealth use. Conclusions: We show that the participants had a feeling of urgency that sparked their motivation to seek nonformal support in the public library. Motivations to participate in the eHealth course stemmed from the need to adapt to the digital society, being a patient or a caregiver, or the need or wish to be independent from others. Participants of the study were mainly older female adults who had native language abilities, up-to-date digital devices, and time. It is likely that other populations experience similar feelings of urgency but have other support needs. Future research should explore the needs and attitudes of nonusers and other users of digital health toward seeking support in eHealth access and use.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".