Barriers to Infertility Treatment: An Integrated Study
Bibliographic record
Abstract
BACKGROUND: Infertility is one of the most important events in life. Despite the negative impact of infertility, a significant number of women struggling to conceive do not consult a physician and do not fallow up infertility treatment. This integrated study aimed to investigate a large amount of factors which influenced discontinuation of infertility treatment. METHODS: This integrated study (qualitative - quantitative study) was done on infertile women who had referred to infertility center in Jahrom University of medical sciences using purposive sampling. In the first study, data were collected from a valid questionnaire with 22 questions in a 5-point likert scale about barriers to infertility treatments and in the second study, as a phenomenology approach, data collection was done using deep unstructured interviews and focused groups were aimed to identify deep individual experiences about it. RESULTS: major barriers to infertility treatments included the probability of treatment failure (52.5%), couple's age and possibility of high risk pregnancy (51.5%), Painfulness of some treatment methods such as laparoscopy (50.5%). Qualitative results led to the identification of three main themes: Nature of treatment, negative thinking, social and cultural factors. CONCLUSION: As a result, we suggest family education and enrichment of cultural context in the field of infertility; infertile people would be willing to pursue infertility treatments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".