O-079 Reflecting on the fertility journey: Insights from couples who discontinued fertility treatment without giving birth
Bibliographic record
Abstract
Abstract Study question How can we enhance the experience of individuals pursuing fertility treatment, from the standpoint of those who have faced unsuccessful outcomes in their treatments? Summary answer The current study identified ways to improve couples’ experience by addressing their unmet needs within the context of fertility care. What is known already Couples seeking fertility treatment face many challenges. Previous studies reported that 20% to 30% of couples decide to discontinue treatment before achieving pregnancy. Exploring ways to improve the experience of individuals seeking fertility treatment is therefore important to reduce treatment dropout. Previous studies have addressed the needs of individuals seeking fertility care, particularly in terms of information and psychosocial support. These studies were predominantely quantitative, limiting the exploration of couples’ subjective perspective. The present study addressed this limitation by using a qualitative research design, allowing for the emergence of other unmet needs and a more nuanced description of couples’ experiences. Study design, size, duration Eleven Canadian couples (n = 22 participants) who stopped fertility treatment without giving birth took part in a qualitative study in Quebec, which was approved by the researchers’ institutional review board. Data were collected between December 2022 and August 2023 as part of a larger qualitative study examining the individual and relational experiences of couples who stopped fertility treatment as well as the clinical context that may have contributed to their decision of discontinuing treatment. Participants/materials, setting, methods Couples who experienced at least one unsuccessful fertility treatment cycle and decided to stop without giving birth were eligible to participate. Ten mixed-gender/sex and one same-gender/sex couples participated in a dyadic online semi-structured interview. Participants were aged between 28 and 47 years old. Four couples were childless. Each interview lasted between 40 and 65 minutes. Interviews were transcribed and verbatims were analyzed by two independent coders using the NVivo software through thematic analysis. Main results and the role of chance During the interviews, partners discussed unmet needs and suggested ways in which these needs could be addressed. Eight themes regarding ways to improve the experience of individuals seeking fertility treatment emerged as as result of the thematic analysis. Namely, (1) providing more information to set realistic expectations (e.g., discussing the demands of fertility treatment and the possibility of treatment not working), (2) improving the physical environment within fertility clinics (e.g., providing a room for couples to gather their thoughts after a difficult announcement), (3) enhancing the sensitivity and availability of healthcare professionals (e.g., showing empathy towards the challenges that couples may face), (4) supplying reliable and accessible information that are expressed in simple non-medical terms (e.g., taking the time to explain medical terms and procedures), (5) promoting patient autonomy regarding fertility care (e.g., suggesting treatment options that take their context into account), (6) providing psychological support (e.g., increasing access to mental health professionals), (7) making patient-centered fertility care more accessible (e.g., offering low-cost psychosocial support), and (8) normalizing the experience of infertility and fertility treatment (e.g., encouraging couples to talk about their experience, offering support groups). Limitations, reasons for caution Participants discontinued fertility treatment without giving birth, making them more likely to have had negative experiences in fertility clinics. Individuals undergoing treatment or who have had successful treatment outcomes might have different perspectives. Additionally, couples came from only four fertility clinics in Quebec, Canada, limiting the heterogeneity of the sample. Wider implications of the findings The present study highlighted areas where services for couples undergoing fertility treatment could be enhanced. Findings may help healthcare providers gain a deeper understanding of the needs of their patients, and inform the development of sensible and reliable resources, which might ultimately help reduce treatment discontinuation. Trial registration number not applicable
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".