Situating Internet Use: Information-Seeking Among Young Women with Breast Cancer
Bibliographic record
Abstract
In recent years considerable attention has been focused on the potential of the Internet as a means of health information delivery that can meet varied health information needs and empower patients. In this article, we explore utilization of the Internet as a means of health information consumption amongst young women with breast cancer who were known Internet users. Focusing on a population known to be competent at using the Internet allowed us to eliminate the digital divide as a possible explanation for limited use of the Internet for health information-seeking. Ultimately, this allowed us to demonstrate that even in this Internet savvy population, the Internet is not necessarily an unproblematic means of disseminating health care information, and to demonstrate that the huge amount of health care information available does not automatically mean that information is useful to those who seek it, or even particularly easy to find. Results from our qualitative study suggest that young women with breast cancer sought information about their illness in order to make a health related decision, to learn what would come next, or to pursue social support. Our respondents reported that the Internet was one source of many that they consulted when seeking information about their illness, and it was not the most trusted or most utilized source of information this population sought.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".