Health Information Seeking in Context: How Women Make Decisions Regarding Hormone Replacement Therapy
Bibliographic record
Abstract
This multimethod study explored women's information seeking behaviour and decision making regarding menopause, hormone replacement therapy (HRT), and use of complementary and alternative medicines (CAM) during menopause. This research was underway during the mass media release of the Women's Health Initiative (WHI) estrogen-progestin trial results, allowing an examination of the impact of this news on women's decisions. There were two studies: first, 20 women who currently were or previously had used HRT were interviewed about their experiences with menopause and HRT-related information seeking and decision making. Following this, 285 demographically representative Canadian women aged 45-65 who were current or former HRT users completed a questionnaire. Results indicate that women's information behaviour differed according to which decision they were making (starting versus stopping HRT, considering CAM), as did the sources they consulted. In general, there has been a paucity of good information to help women who are deciding to stop HRT. The types and sources of CAM information often are found to be less than credible and helpful. When information is lacking, women rely on informal sources, and on their own judgement, to make decisions. The results are discussed in the context of information behaviour and help-seeking theory.
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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 | 0.002 | 0.011 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".