Bibliographic record
Abstract
Women living in a rural Canadian county were interviewed about how they locate health information. The experiences they described raise interesting questions about the efficacy of government sponsored e-health initiatives, particularly when such programs are intended to compensate individuals who live in remote communities for lack of access to health care services. Most of the women in the study undertake considerable health-related information gate-keeping for themselves and on behalf of family members and others in their personal networks. They seek and assess information from a wide variety of sources, some of which they locate via the Internet, and they balance what they learn against their experiences with the formal health system. The women’s accounts focused repeatedly on the quality of their relationship with those to whom they turn for assistance, although the actual roles of helpers, whether physicians, friends, librarians, or staff in health food stores, often appeared to be incidental. Instead, helpers’ perceived effectiveness seemed to depend largely on how well they express care when information is exchanged. Several women also reported that they had diagnosed and even treated themselves, sometimes on the basis of information gathered from the Internet. These and other findings are discussed with respect to public policy concerning consumer health information and the potential role of public libraries in the provision of health information programming in rural communities.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".