Reducing Negative Outcomes of Online Consumer Health Information: Qualitative Interpretive Study with Clinicians, Librarians, and Consumers (Preprint)
Bibliographic record
Abstract
BACKGROUND There has been an exponential increase in the general population’s usage of the internet and of information accessibility; the current demand for online consumer health information (OCHI) is unprecedented. There are multiple studies on internet access and usage, quality of information, and information needs. However, few studies explored negative outcomes of OCHI in detail or from different perspectives, and none examined how these negative outcomes could be reduced. OBJECTIVE The aim of this study was to describe negative outcomes associated with OCHI use in primary care and identify potential preventive strategies from consumers’, health practitioners’, and health librarians’ perspectives. METHODS This included a two-stage interpretive qualitative study. In the first stage, we recruited through a social media survey, a purposeful sample of 19 OCHI users who had experienced negative outcomes associated with OCHI. We conducted semistructured interviews and performed a deductive-inductive thematic analysis. The results also informed the creation of vignettes that were used in the next stage. In the second stage, we interviewed a convenient sample of 10 key informants: 7 health practitioners (3 family physicians, 2 nurses, and 2 pharmacists) and 3 health librarians. With the support of the vignettes, we asked participants to elaborate on (1) their experience with patients who have used OCHI and experienced negative outcomes and (2) what strategies they suggest to reduce these outcomes. We performed a deductive-inductive thematic analysis. RESULTS We found that negative outcomes of OCHI may occur at three levels: internal (such as increased worrying), interpersonal (such as a tension in the patient-clinician relationship), and service-related (such as postponing a clinical encounter). Participants also proposed three types of strategies to reduce the occurrence of these negative outcomes, namely, providing consumers with reliable OCHI, educating consumers on how to assess OCHI websites, and helping consumers present and discuss the OCHI they find with a health professional in their social network or a librarian for instance. CONCLUSIONS We examined negative outcomes associated with using OCHI from five complementary perspectives (consumers, family physicians, pharmacists, nurses, and health librarians). We identified a construct of OCHI use–related tension that included and framed all negative outcomes. This construct has three dimensions (three interdependent levels): internal, interpersonal, and service-related tensions. Future research can focus on the implementation and effectiveness of the proposed strategies, which might contribute to reducing these tensions.
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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 | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".