Couples’ experiences of pregnancy resulting from assisted reproductive technologies: A qualitative meta-synthesis
Bibliographic record
Abstract
Background: Across the world, a growing number of couples are directly or indirectly affected by infertility. Advances in assisted reproductive technologies are now enabling many of them to plan to have a child. As pregnancies resulting from assisted reproductive technologies are being experienced by a growing number of couples, it is important to better understand their experiences and the difficulties they encounter during the prenatal period. Objective: This literature review aims to synthesize the qualitative results of primary studies in order to better understand couples' experiences of pregnancy resulting from assisted reproductive technologies, after having experienced infertility. Design: A meta-synthesis was conducted in accordance with the guidelines put forth by Sandelowski and Barroso in order to carry out an integrative analysis of the knowledge resulting from qualitative studies on this phenomenon. Data sources: The literature search was carried out between October and November 2020. Seven electronic databases were searched (CINAHL, Medline, PsycNet, SCOPUS, ScienceDirect, CAIRN, ERUDIT) and 14 eligible articles were selected. Review methods: The data from the "results" and "findings" sections of each of the articles were synthesized through thematic analysis to examine and summarize the topics found in the articles selected and develop the main themes. Results: The thematic analysis is structured around four themes to describe and interpret different aspects of the experience of pregnancy resulting from ART, namely: 1) travelling a long and complex journey; 2) moving on with paradoxical emotions; 3) struggling with an emerging identity; and 4) reorganizing relationships. These results shed light on the complex issues faced by couples previously affected by infertility during a pregnancy resulting from assisted reproductive technologies. Conclusion: The journey of a pregnancy resulting from assisted reproductive technologies must be recognized in order to offer support in line with the challenges faced by couples. This research will allow for a deeper understanding of women's experience, as well as that of their partner's, in a more specific way, and to better understand the impact on family and loved ones.
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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 | 0.049 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".