Factors of non-adherence to therapy in chronic patients with pathologies covered by specific legislation in Portugal
Bibliographic record
Abstract
Medication adherence is a multidimensional phenomenon determined by the interaction of factors of diverse nature. The World Health Organization classified in five groups the reasons for non-adherence to therapy, related to, patient, disease, therapy, health system and socioeconomic factors [1]. Objective/Aim: To identify the most prevalent extrinsic and intrinsic factors for non adherence to therapy and to verify the existing differences taking into account the socioeconomic variables. Methods: A random probabilistic sample of 141 outpatient suffering from pathologies covered by specific legislation with dispensing medicines at the hospital pharmacy, treated at the Local Health Unit of the Northeast in Portugal, was selected. The sample included patients with Chronic Renal Insufficiency (n=89), Rheumatoid Arthritis and Psoriatic Arthritis (n=29), Multiple Sclerosis (n=15), Amyotrophic Lateral Sclerosis (n=3), Hepatitis C Virus (n=3), Hepatic disease (n=1) and Gaucher Syndrome (n=1). To collect the data, was applied a questionnaire, by interview, that included socioeconomic variables and a list of non-adhesion factors adapted from Cabral and Silva [2], between July 2017 and April 2018. The list of factors for non-adherence to the therapy consisted of 35 factors that were later aggregated into three dimensions. The first dimension "extrinsic factors", consisted of 11 reasons that could lead patients not to follow completely the indications recommended by the doctor. The second dimension "intrinsic factors" was constituted by 20 factors related to the characteristics of the medicines and the therapeutics. The SPSS 24.0 software was used to analyse the data. The internal consistency was analysed through Alpha Cronbach. For the comparison of groups, the nonparametric Mann-Whitney test was used at a significance level of 5%. Results: In the "extrinsic factors" dimension, the three most prevalent factors were "patient does not like to have the trips to go to consultations" (39%), "patient does not like to take medications" (37.6%) and "patient does not like to think he is ill "(31.9%). It was the female patients with the lowest level of education and the lowest income who were most likely to leave the treatment. The "intrinsic factors" that stand out were: "the schedule of the shots" (36.9%), "drugs were difficult to take" (29.8%) and "treatment duration was long" (29, 1%). It was women, aged 65 years old or more, without professional occupation, with lower levels of income and schooling who were less compliant with medical indications. Conclusion: The socioeconomic variables are differentiated from the non-compliance by the medical indications. References (Vancouver Style): 1. World Health Organization. Adherence to long-term therapies: Evidence for action. Geneva: World Health Organization; 2003. [Access date may, 2017]. Available from:http://www.who.int/chp/knowledge/publications/adherence_report/en/ 2. Cabral, M., Silva, P. A adesão à terapêutica em Portugal: Atitudes e comportamentos da população portuguesa perante as prescrições médicas. APIFARMA, 2010.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".