Bibliographic record
Abstract
The current research was designed to examine health anxiety among individuals with Lynch syndrome; a genetic predisposition to adult onset cancers. This research had two aims: 1) To examine the severity of health anxiety in Lynch syndrome patients and identify predictors and consequences associated with health anxiety, and 2) To examine the additional impact health anxiety has on parents with Lynch syndrome. Two studies were conducted. In Study I, 209 individuals with Lynch syndrome, selected from a genetic cancer registry, completed self-report measures assessing health anxiety, medical and psychological variables, and medical service utilization. Results indicated that 30% of participants reported clinically significant levels of health anxiety. Regression analyses revealed that younger age, greater depression, anxiety, worry interference and emotional preoccupation coping were predictive of increased health anxiety. Increased health anxiety was associated with greater overall medical service utilization; specifically, visits to gastroenterologists and emergency departments. In Study II, purposive sampling was used to identify parents from Study I who reported the highest and lowest health anxiety. Twenty-one individuals completed semi-structured telephone interviews about their experience of being a parent with Lynch syndrome, their concerns of potentially passing down the genetic mutation to their children, and their perceptions of their children’s health. Qualitative content analysis using a template coding approach was used to examine the differences between parents with high and low health anxiety. Findings revealed that the most prevalent difference was in relation to parent’s perceptions of their personal health. Those with high health anxiety experienced worries that were more extreme, demonstrated a hypervigilance towards physical symptoms, discussed the emotional and psychological consequences of Lynch syndrome as more negative and severe, and had a tendency to engage in more dysfunctional coping strategies. Unexpectedly, with regards to their perceptions of their children, the parents in the high and low health anxiety groups exhibited similar worries. Taken together, the findings from Studies I and II suggest that health anxiety is of clinical significance for individuals with Lynch syndrome. Accurately identifying and treating health anxiety among this population may be one avenue to reduce the distress experienced by Lynch syndrome carriers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".