L’identité narrative dans la prise en charge psychologique des jeunes issus de la conception médicalisée
Bibliographic record
Abstract
The story of becoming a parent is often couched in the language of desire built on fantasies. And although such idyllic accounts tend to conceal much more difficult stories about becoming parents, nevertheless assisted reproductive technology has given new hope to many couples experiencing infertility. In the course of our work as professional psychologists, we recorded the reflections of young women between the ages of 15 and 25 who were conceived using assisted reproductive technology. To understand how the circumstances of their conception impacted their narrative identity, we adopted a clinical approach that involved having these young women share their life narratives, starting with their conception stories. Based on our clinical observations, the article explores a five-stage method for facilitating narrativity, consisting of the interview process, projective mechanisms, the creation of an autobiographical timeline, and, finally, the act of writing a life story. This multifaceted approach makes it possible to address the impact of conception stories on narrative identity from both a diachronic and synchronic perspective. Finally, we conduct a theoretical and clinical assessment of this promising methodology, which primarily aims to give young people the tools and support they need to share their life narratives in the context of therapy sessions—a process likely to promote better mental health through more harmonious identity development.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".