Building Social Support: Disclosure and Communication Processes Between IVF Patients and Peers in Canada
Bibliographic record
Abstract
Infertility, and the choice to attempt assisted reproductive technology, is the source of significant stress for patients pursuing in vitro fertilization (IVF), and this compels many to identify and leverage psychosocial supports. Because the quality of social support individuals receive depends on the nature of the communication they share with the receiver, it is important to consider how disclosure builds social support. We explored the IVF patient and peer communication process and the disclosure of fertility-related and non-fertility-related information by conducting 23 interviews with first-time and recurring IVF patients. Results show that IVF patients share natural, immediate, and backward disclosure transitions; share a mutual understanding of engagement boundaries; have a propensity for reciprocal sharing; and prefer digital communication for their interactions. While participants reported disclosing a wide range of aspects of their condition and its treatment, such as treatment protocol, diagnosis/IVF attempts, medication and injections, financial questions, marital adjustment, family and social acceptance, emotional adjustment, and treatment milestones, they also reported a tendency to distance themselves during the post–embryo transfer waiting period and avoided sharing other aspects of their lives. Future support strategies should frame patient–peer support as a pragmatic channel that can adapt depending on disclosure and communication preferences of patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".