PS-127 Child Disability Announcement: Parents’ Impact
Bibliographic record
Abstract
Introduction The announcement of a disability in a child or infant is too delicate because it is crucial for the future of the child and his family. Materials and methods We conducted a descriptive and analytical study about parents of 83 children with disabilities, followed in the neonatal unit of Sfax and supported in one of the associations for the disabled. Semi-structured interviews were conducted by a psychiatrist or psychologist to complete a questionnaire. Results The paediatrician was the first preacher of disability in 48% of cases. This announcement took place at the outpatient in 78% of cases during the 40th day consultation in more than two thirds of cases. The mother was lonely during the announcement in 53% of cases. Talks criminalising the parents in the aetiology of disability were present in one third of cases and those downplaying the role of parents in the care were noted in a quarter of cases. The immediate experience of the parents was dominated by anxiety in 62.5% of cases and depressive symptoms in third of cases. Contributing factors to these situations were: - The male child - Talks criminalising parents in the aetiology of disability - trivialising attitude - A long conversation - An announcement in consultation Conclusion The announcement of disability requires much more interest in our region. Our purpose is to translate a message of hope in steed of drama.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.001 |
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".