Improving Illness Perception Through Self-Care Behavior Training: A Randomized Controlled Trial
Bibliographic record
Abstract
This study aimed to investigate the effects of Self-Care Behavior Training intervention on individuals' perception of illness by comparing experimental and control groups over time, with measurements taken at pre-test, post-test, and follow-up stages. The study involved a total of 40 participants, divided equally into experimental and control groups. Descriptive statistics were used to analyze the baseline characteristics of both groups. A mixed-design ANOVA was conducted to explore the effects of time (pre-test, post-test, follow-up), group (experimental, control), and their interaction on the perception of illness. The Bonferroni post-hoc test was applied to assess differences between individual time points for the experimental group. The experimental group showed a significant improvement in illness perception from pre-test to post-test (mean difference = 26.73, p=0.001) and maintained this improvement at follow-up (mean difference from pre-test = 27.74, p=0.001). There was no significant change between post-test and follow-up (mean difference = 1.01, p=1.00), indicating the intervention's lasting effect. The control group did not demonstrate significant changes over time. The ANOVA results confirmed significant effects of time (F=10.83, p<0.01), group (F=10.55, p<0.01), and their interaction (F=10.03, p<0.01) on illness perception. The intervention significantly improved the experimental group's perception of illness, with these improvements sustained over time. The control group's stable illness perception across all stages emphasizes the intervention's efficacy. These findings suggest that targeted interventions can effectively alter illness perceptions, which may have important implications for patient care and recovery processes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".