Factors affecting self-management in Iranian tuberculosis patients: A path analysis model
Bibliographic record
Abstract
Abstract Background Tuberculosis is a chronic infectious disease that, if not treated and managed properly, increases the Multi Drug Resistance (MDR). Self-management behaviors can reduce the progression of illness. Although various factors affect self-management, no study has not been conducted on the self-management tuberculosis through path analysis. This study evaluated the factors affecting self-management in tuberculosis patients using path analysis. Methods This study was a cross-sectional study and was done on 133 non prisoner tuberculosis (TB) patients that referred to all health centers in Karaj, Iran from March 2016 to February 2017.The structured questionnaire was applied. Data were analyzed with SPSS-17 and Lisrel 8.8, utilizing statistical path analysis to evaluate the relationships between self-management and its related factors. Results 52.3% of the participants in the study were female and 47.7% were male. Respondents of this research were 46.9% smear-positive, 9.4% smear- negative, 43.8% extra-pulmonary TB. Fit indices confirmed the model fitness and logical relationships between the variables according to the conceptual model (X2=49.80, df=25). The final path model showed that the age (B=0.84), attitude (B=0.10), marital status (B=0.04) and house condition (B=0.03) impact self-management just through direct path. Knowledge (B=0.83) and education (0.16) affect the self-management through both direct and indirect paths. The education indirectly affects the self-management through both knowledge and attitude. knowledge indirectly impact self-management through attitude. In other words knowledge and attitude mediate the relationship between some factors and self-management. Conclusions This study provided an empirical model that illustrates the relationships between self-management and related factors in TB Patients. The most important factor that affects self-management through both direct and indirect paths was knowledge. The knowledge can be the target of interventions in support of self-management
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".