Understanding the Blogging Practices of Women Undergoing In Vitro Fertilization (IVF): A Discourse Analysis of Women’s IVF Blogs
Bibliographic record
Abstract
Infertility and its associated treatments, including in vitro fertilization (IVF), can have a profound impact on the emotional health and well-being of women desiring to become mothers. Researchers have measured the impact of infertility and described the experience of infertility and its treatment, leaving the rich descriptions of the IVF experience as captured in women’s blogs to be explored. This discourse analysis describes the blogging practices of women undergoing IVF, exploring both the content and function of the IVF blog discourse. Data were collected from the text of seven women’s blogs (n=1,149 blog posts) and resulted in four main functions of the discourse: creation of and connection to a community, emotional support, blogging as therapy, and creation of an IVF resource. Findings suggest that blogging can have a positive impact on the psychosocial consequences experienced by women in fertility treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".