Consumer use of the internet for health information: a survey of primary care patients
Bibliographic record
Abstract
Abstract An increasing number of patients use the internet to obtain information about health. Although some information is available about how health professionals use the internet, little is known about how patients utilize this information. Some patients may actively seek information to assume more responsibility for their health. However, others may feel obliged to do so because of failing confidence in health care provision. Health professionals have the potential to assist patients to make sense of health information from the internet; however, they may not necessarily welcome this role. This study aims to evaluate patients’ use of such information in a primary care setting. The sample consisted of adult patients (n = 851) from two general practice populations at different levels of the socio‐economic spectrum in South Wales (UK). Patients were surveyed by questionnaire about the health information they use, including the internet. The majority of patients preferred to use their general practitioner as the main source of health information. The internet was jointly the second preferred source for information about an illness (6%). Just over half (51%) of patients in this study had access to the internet, of which about half use it to access health information. Just under a quarter (24%, n = 55) of health internet users had discussed information accessed from the internet during a subsequent consultation with a health professional. Of these, three‐quarters felt more prepared and able to participate in decision‐making about treatments. This study provides a greater understanding of how patients are making use of health‐related information from the internet. These findings can be used to help prepare health care professionals for dealing appropriately with internet‐informed patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".