Mothers’ Perspectives on Their Child's Mental Illness as Compared to Other Complex Disorders in Their Family: Insights to Inform Genetic Counseling Practice
Bibliographic record
Abstract
To facilitate the development of a therapeutic alliance in genetic counseling, it is important that the counselor understands how families might perceive the condition that constitutes the reason for the referral. Through training and professional practice, genetic counselors develop a thorough understanding of families' perceptions of the conditions that are common indications for genetic counseling. But, for referral indications that are less frequent, like serious mental illnesses, genetic counselors may feel less confident in their understanding of the family's experience, or in their ability to provide psychosocial support when serious mental illness is reported in a family history. This may impede the establishment of a therapeutic alliance. As research shows that most referrals for genetic counseling related to serious mental illness are for female first-degree family members of affected individuals, we sought to explore how this group perceives serious mental illness. To provide a frame of reference with which genetic counselors may be more familiar, we explored how women perceived serious mental illness compared to other common complex disorders in their family. We conducted semi-structured interviews with women who had a child with a serious mental illness (schizophrenia, schizoaffective disorder, bipolar disorder) and a first-degree relative with another common complex disorder (diabetes, heart disease, cancer). Interviews were transcribed and subjected to thematic analysis. Saturation was reached when nine women had participated. Serious mental illness was perceived as being more severe and as having a greater impact on the family than diabetes, heart disease, or cancer. Themes identified included guilt, stigma, and loss. Some of the most important issues that contribute to mothers' perceptions that serious mental illness is more severe than other common complex disorders could be effectively addressed in genetic counseling. Developing a heightened awareness of how family members experience a relative's mental illness may help genetic counselors to be better able to provide psychosocial support to this group, whether serious mental illness constitutes the primary reason for referral or appears in the family history during counseling for a different referral reason.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".