“So Much of This Story Could Be Me”: Men’s Use of Support in Online Infertility Discussion Boards
Bibliographic record
Abstract
Past research has suggested that social support can reduce the negative psychological consequences associated with infertility. Online discussion boards (ODBs) appear to be a novel and valuable venue for men with fertility problems to acquire support from similar others. Research has not employed a social support framework to classify the types of support men are offered and receive. Using template, content, and thematic analysis, this study sought to identify what types of social support men seek and receive on online infertility discussion boards while exploring how men having fertility problems use appraisal support to assist other men. One hundred and ninety-nine unique users were identified on two online infertility discussion boards. Four types of social support (appraisal, emotional, informational, and instrumental) were evident on ODBs, with appraisal support (36%) being used most often to support other men. Within appraisal support, five themes were identified that showed how men communicate this type of support to assist other men: "At the end of the day, we're all emotionally exhausted"; "So much of this could be me, infertility happens more than you think"; "I've also felt like the worst husband in the world"; "It's just something that nobody ever talks about so it's really shocking to hear"; "I say this as a man, you're typing my thoughts exactly." These findings confirm how ODBs can be used as a potential medium to expand one's social network and acquire support from people who have had a similar experience.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".