Factors of non-adherence to therapy in chronic patients with pathologies covered by specific legislation in Portugal
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».