Exploring the link between chronic illness adaptation and health anxiety: insights from a primary care outpatient clinic in Turkey
Bibliographic record
Abstract
Background.The rising life expectancy and advancements in health care have led to an upsurge in chronic diseases, highlighting the emergence of significant societal and public health challenges.Objectives.We aimed to examine the effects of the health anxiety of individuals with chronic diseases on their adjustment to their chronic diseases.Material and methods.This study was designed as observational, descriptive and cross-sectional.The population of the study consisted of individuals who had a chronic disease and were treated in a family medicine outpatient clinic for any reason between February and March 2022.The sampling method was determined to be probabilistic and sequential, and a total of 107 individuals aged 18 years and older who volunteered to participate in the study with these characteristics were included in the study.Data was collected using a questionnaire that included descriptive characteristics of the patients and researcher-generated information about the disease, as well as the Chronic Disease Adjustment Scale and the Health Anxiety Scale.Results.A statistically significant inverse relationship was found between patients' scores on the Health Anxiety Scale and scores on the Chronic Disease Adjustment Scale and the psychological and social adjustment subscales. Conclusions.With the interventions to be made on patients' health anxiety, it will be possible to adapt the patients to their chronic diseases so that regular drug use and social well-being will be possible for the patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".