Primary Health Care Nurses’ Experiences of Consultations With Internet-Informed Patients: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Most people in modern societies now use the Internet to obtain health-related information. By giving patients knowledge, digital health information is considered to increase patient involvement and patient-centered interactions in health care. However, concerns are raised about the varying quality of health-related websites and low health literacy in the population. There is a gap in the current knowledge of nurses' experiences with Internet-informed patients. OBJECTIVE: The objective of this study was to explore primary health care nurses' experiences of consultations with patients who present health-related information from the Internet. METHODS: This is a qualitative study based on interviews with 9 primary health care nurses. Data were analyzed using qualitative content analysis. Results are reported according to the consolidated criteria for reporting qualitative research guidelines. RESULTS: The phenomenon of Internet-informed patients was considered to change the usual rules in health care, affecting attributes and actions of patients, patterns of interactions in consultations, and roles of nurses and patients. Three categories were identified: (1) Facing the downsides of Googling, (2) Patients as main actors, and (3) Nurse role challenged. Although the benefits of health-related Internet information were described, its negative consequences were emphasized overall. The problems were mainly ascribed to inaccurate Internet information and patients' inability to effectively manage the information. CONCLUSIONS: Our study suggests ambivalent attitudes among nurses toward health-related Internet information. In order to promote equitable care in the digital era, increased awareness in health care about useful strategies for overcoming the difficulties and embracing the benefits of conferring with Internet-informed patients seems to be a legitimate goal.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 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".