Parental psychosocial aspects and stressors involved in the management of inborn errors of metabolism
Bibliographic record
Abstract
Parents of children with inborn errors of metabolism (IEM) face numerous psychosocial challenges. An increased understanding and awareness of these stressors can ensure better overall outcomes for the entire family. We conducted semi-structured, in-person interviews with ten parents to identify psychosocial stressors, strategies, and supports they utilized to overcome their challenges. Our interview guide was designed to elicit familial experiences during the pre- and post-diagnosis periods. The themes and sub-themes were identified through qualitative descriptive textual analysis of audio-recorded transcripts. Major themes identified include ambiguity of illness, changing family and spousal dynamics, and navigating the healthcare system. Sub-themes revolved around disease effects, psychological stressors, health systems, support, and facing the disease. Healthcare professionals have an opportunity to minimize the impact of negative emotional outcomes by assisting families as they navigate the experience of having a child with an IEM. Our findings can be used to develop and continue a more well-rounded, family-oriented framework for IEM management.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".