How do prenatal people describe their experiences with anxiety? a qualitative analysis of blog content
Bibliographic record
Abstract
Abstract Background Despite elevated prevalence rates of prenatal (antenatal) anxiety across studies (13–21%), and prenatal people’s use of the Internet to search for pregnancy-related information and support, research investigating prenatal people’s experiences with online mental health communication, such as blogs, is lacking. This study examined blog entries focused on anxiety in pregnancy to better understand prenatal people’s Internet discourse concerning their experiences with anxiety. Methods A Google search using the keywords “anxiety,” “pregnant,” and “blog” resulted in N = 18 blogs that met inclusion criteria (public blog written in English describing a personal experience with prenatal anxiety in 250 words or more). Blog content was analyzed using a thematic analytic approach based on grounded theory principles. Results Three main themes capturing prenatal people’s experiences with anxiety as written in public blog content were developed from qualitative analyses: 1) etiology (subthemes: before pregnancy, during the current pregnancy, related to a previous pregnancy), 2) triggers (subthemes: uncertainty, perceived lack of control, and guilt and shame for not having a normal pregnancy), and 3) symptoms (subthemes: intertwined emotional, cognitive and physical symptoms, in addition to behavioural symptoms). Conclusions Our findings demonstrate a need for perinatal professionals to address anxiety symptoms and triggers in pregnancy. One way to address this may be by providing credible information regarding prenatal mental and physical health to pregnant people through online mediums, such as blogs. Bloggers often discussed experiencing a combination of emotional, cognitive, physical, and behavioural symptoms, which suggests that medical and mental health professionals should work collaboratively to provide care for prenatal people experiencing anxiety. Furthermore, Cognitive Behavioural Therapy (CBT) addresses these types of symptoms, which suggests that interventions developed or adapted to meet this populations’ needs could employ this therapeutic approach. Future research should explore the reasons why prenatal people experiencing anxiety engage with blogs, the characteristics of bloggers and readers, the impact of the blogging experience on both the blogger and their audience, and the information quality of blog content.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".