Internet-Based Information Behavior After Pregnancy Loss: Interview Study
Bibliographic record
Abstract
BACKGROUND: Information behavior describes all human behaviors in relation to information. Individuals experiencing disruption or stigma often use internet-based tools and spaces to meet their associated information needs. One such context is pregnancy loss, which, although impactful and common, has been absent from much of feminist and reproductive health and information behavior scholarship. By understanding information behavior after pregnancy loss and accounting for it in designing internet-based information spaces, we can take a meaningful step toward countering the stigma and silence that many who experience such loss endure, facilitate coping, and make space for diverse pregnancy narratives in our society. OBJECTIVE: This study's objective is to provide a characterization of internet-based information behavior after pregnancy loss. METHODS: We examined internet-based information behavior after pregnancy loss through 9 in-depth interviews with individuals residing in the United States. We analyzed the data by using open and axial coding. RESULTS: We identified the following three themes in relation to participants' information behavior in internet-based spaces: needed information types, information-related concerns, and information outcomes. We drew from information behavior frameworks to interpret the processes and concerns described by participants as they moved from recognizing information needs to searching for information and to using information and experiencing outcomes. Specifically, we aligned these themes with information use concepts from the information behavior literature-information search, knowledge construction, information production, information application, and information effects. Participants' main concerns centered on being able to easily find information (ie, searchability), particularly on topics that had already been covered (ie, persistence), and, once found, being able to assess the information for its relevance, helpfulness, and credibility (ie, assessability). We suggest the following design implications that support health information behavior: assessability, persistence, and searchability. CONCLUSIONS: We examined internet-based information behavior in the context of pregnancy loss, an important yet silenced reproductive health experience. Owing to the prevalence of information seeking during pregnancy, we advocate that generic pregnancy-related information spaces should address the needs related to pregnancy loss that we identified in addition to spaces dedicated to pregnancy loss. Such a shift could not only support those who use these spaces to manage pregnancies and then experience a loss but also help combat the silence and stigma associated with loss and the linear and normative narrative by which pregnancies are often represented.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".