Attitudes about Infertility among Male and Female Saudi Medical Students
Bibliographic record
Abstract
Gender biases impact doctors’ advising on infertility, thereby shaping treatment recommendations and patient health outcomes. This study explored the roles attributed to gender in the personal opinions of male and female medical residents in Saudi Arabia. This study used content and Appraisal analyses to explore attitudes realised by 85 female and 81 male Saudi medical interns about infertility. Content contained six themes, including infertility, psychology, children, marriage, divorce and religion. Both male and female participants understood women as the cause of and person responsible for dealing with infertility. Males focused on medical treatments, females on folk medicines. Female appraisals were mainly negative, male appraisals mainly positive. Strong co-frequencies were found for females between divorce and misery, and folk medicine and capacity, and for males between medical treatments and capacity, and children as emotionally fulfilling to women and normality. Both groups understood infertility primarily as a social and religious more than a medical issue. Gender biases, and contradictions in attributed gender roles were evident in how both groups discussed infertility. International institutions teaching healthcare communication must emphasise awareness of how gender stereotyping and cultural factors impact infertility advising and treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".