Understanding the Impact of Obsessive-Compulsive Personality Traits on the Management of Chronic Illness: A Phenomenological Study
Bibliographic record
Abstract
This study aims to explore the impact of obsessive-compulsive personality traits on the management of chronic illness. A qualitative research design was used, focusing on semi-structured interviews with 20 participants who exhibited obsessive-compulsive personality traits and were managing chronic illnesses. Participants were recruited from outpatient clinics, support groups, and online forums. Data collection continued until theoretical saturation was achieved. The interviews were transcribed verbatim and analyzed using NVivo software. The analysis involved coding and identifying themes and subthemes to capture the essence of participants' experiences. The analysis revealed three primary themes: rigidity in daily routines, health anxiety and hyper-vigilance, and emotional and psychological impact. Participants described a reliance on structured schedules and routines, which, while helpful in managing their illness, often led to significant stress when disrupted. Health anxiety manifested as constant monitoring and fear of contamination, contributing to heightened stress and anxiety. The emotional toll included feelings of inadequacy, self-criticism, and emotional exhaustion. These findings align with previous research on the pervasive effects of obsessive-compulsive traits on mental health and daily functioning, emphasizing the dual role of these traits in facilitating and complicating chronic illness management. Obsessive-compulsive personality traits significantly impact the management of chronic illnesses, influencing behaviors and emotional well-being. The study highlights the need for tailored interventions that address both the psychological and practical aspects of illness management. Future research should include larger and more diverse samples and explore the effectiveness of various interventions. Healthcare providers should develop individualized care plans to support patients with these traits, enhancing their quality of life and illness outcomes.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".