[Health-related use of the Internet in the Norwegian population].
Bibliographic record
Abstract
BACKGROUND: The use of the Internet for health purposes increases in the Norwegian population, more in some demographic groups than in others. In this questionnaire-based study, we explore the use of the Internet for such purposes. MATERIAL AND METHOD: 1007 Norwegians aged 15 years and older were interviewed by telephone in October 2005. RESULTS: 58% of the respondents in 2005 had used the Internet for health purposes, compared to 31% in 2001. Having visited the GP last year, being female, being young, living in a urban area, and having a white-collar occupation were positively related to the use of the Internet for health purposes. 37% of the respondents considered the Internet to be an important or very important source of health information. 72% considered face-to-face communication with health care personnel to be important or very important. Nearly a quarter of the users (23%) reported that they had felt reassured by health information found on the net, whereas 10 % experienced increased anxiety from the same type of information. CONCLUSIONS: Norwegians' use of the Internet for health purposes continues to grow, but doctors and other health care personnel remain the most important sources of health information in the Norwegian population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".