The Effect of Using the Care Model of "Sensitization" on Medication Adherence in Asthmatic Patients
Bibliographic record
Abstract
INTRODUCTION: Asthma is a prevalent disease with a multitude of complication. The present research was aimed at investigating the effects of implementing sensitization care model on medication adherence in asthmatic patients. METHODS & MATERIALS: In this interventional study, 74 subjects were selected using accessibility sampling method and were randomly classified into 2 separate groups, intervention (37 subjects) and control group (37 subjects). Data were collected with questionnaire. A month later, the subjects in both groups (control and intervention) completed questionnaire again. The collected data were analyzed using SPSS software, Independent t-test, Paired t-test and Chi-Square. RESULTS: Data analysis showed that the average age of participants was 41 years (range 21-83). The most and lowest frequencies of marital status in both sexes were 66.2% married and 33.8% single, respectively. There was no significant difference before intervention between two groups. However, after the intervention, there was a significant difference between the two groups (P=0.0001). DISCUSSION: The results of the present study showed that implementing sensitization care model had a positive effect on medication adherence among asthmatic patients. Therefore, application of this model is recommended to care and treatment asthmatic 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".