Qualitative study: patients’ enduring concerns about discussing internet use in general practice consultations
Bibliographic record
Abstract
OBJECTIVES: To examine patients' accounts of their use of the internet before seeing a general practitioner (GP) using thematic analysis of semistructured interviews. DESIGN: Qualitative semistructured interview study with transcripts analysed thematically. SETTING: Primary care patients consulting with 10 GPs working at 7 GP practices of varying sizes and at a range of locations around London and the Southeast of England. PARTICIPANTS: 28 adult patients: 16 women and 12 men ranging in age from 18 to 75 from a range of self-defined ethnic backgrounds. Participants were selected based on instances when the patients reported having used the internet before the consultation, when patients referred to the internet in the consultation or when the physician used the internet or made reference to it during the consultation. RESULTS: Patients report that they can find health information online that they believe is reliable and helpful for both themselves and their GP. However, they report uncertainty about how to share internet-based findings and reluctance to disclose their efforts at researching health issues online for fear of appearing disrespectful or interfering with the flow of the consultation. CONCLUSIONS: Despite the democratisation of access to information about health due via the internet, patients continue to experience their use of the internet for health information as a sensitive and potentially problematic topic. The onus may well be on GPs to raise the likelihood (without judgement) that patients will have looked things up before consulting and invite them to talk about what they found.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".