Examining the effect of patient personality types and coping styles on outcomes of genetic counseling
Bibliographic record
Abstract
Both empirical data and genetic counselors' clinical experience suggest that patients differ in the extent to which they benefit from genetic counseling (GC). Understanding the origins of these differences could help adapt services to ensure that all patients benefit fully, and potentially inform triage. Although patient personality dimensions and coping styles have been shown to influence outcomes of other psychological interventions, they have remained largely unexplored in relation to GC outcomes. We conducted an exploratory, descriptive study to assess relationships between patient personality dimensions, coping styles, and outcomes of GC. We recruited patients from a psychiatric genetics clinic who had - in the prior 7 years - completed the GC Outcomes Scale (GCOS, a measure of empowerment) immediately prior to, and approximately one month after their appointment, and asked them to complete validated measures of personality and coping style. Interactions between each personality dimension or coping style and GCOS score were assessed using mixed-effects linear regression models. Among the 169 participants, GCOS score increased by an average of 16.48 points (SD = 12.59). Though extraversion, conscientiousness, neuroticism, and all three coping styles significantly predicted GCOS score (p < 0.02), there was no relationship between these variables and time. For example, though a high score on conscientiousness predicted higher GCOS scores, it did not predict greater change in GCOS - people with higher scores on this dimension of personality had higher GCOS scores both pre- and post- GC. These preliminary data suggest that genetic counseling may increase empowerment regardless of personality dimensions and coping styles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".