Journal article
Content analysis of antiretroviral adherence enhancing interview reports.
- Kamal S Community Pharmacy, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Community Pharmacy, Department of Ambulatory Care & Community Medicine, University of Lausanne, Geneva, Switzerland.
- Nulty P Centre for Research in Arts, Social Science, and Humanities, University of Cambridge, Cambridge, UK.
- Bugnon O Community Pharmacy, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Community Pharmacy, Department of Ambulatory Care & Community Medicine, University of Lausanne, Geneva, Switzerland.
- Cavassini M Infectious Disease Service, Lausanne University Hospital and University of Lausanne, Geneva, Switzerland.
- Schneider MP Community Pharmacy, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Community Pharmacy, Department of Ambulatory Care & Community Medicine, University of Lausanne, Geneva, Switzerland. Electronic address: marie-paule.schneider@hospvd.ch.
- 2018-06-07
Published in:
- Patient education and counseling. - 2018
Antiretrovirals
Electronic monitoring
HIV
Inter-professional collaboration
Medication adherence
Medication adherence program
Mixed-method
Motivational interviewing
Pharmacist
Physician
Adult
Anti-Retroviral Agents
Female
HIV Infections
Humans
Interprofessional Relations
Male
Medication Adherence
Middle Aged
Pharmacists
Physicians
Social Stigma
English
OBJECTIVE
To identify factors associated with low or high antiretroviral (ARV) adherence through computational text analysis of an adherence enhancing programme interview reports.
METHODS
Using text from 8428 interviews with 522 patients, we constructed a term-frequency matrix for each patient, retaining words that occurred at least ten times overall and used in at least six interviews with six different patients. The text included both the pharmacist's and the patient's verbalizations. We investigated their association with an adherence threshold (above or below 90%) using a regularized logistic regression model. In addition to this data-driven approach, we studied the contexts of words with a focus group.
RESULTS
Analysis resulted in 7608 terms associated with low or high adherence. Terms associated with low adherence included disruption in daily schedule, side effects, socio-economic factors, stigma, cognitive factors and smoking. Terms associated with high adherence included fixed medication intake timing, no side effects and positive psychological state.
CONCLUSION
Computational text analysis helps to analyze a large corpus of adherence enhancing interviews. It confirms main known themes affecting ARV adherence and sheds light on new emerging themes.
PRACTICE IMPLICATIONS
Health care providers should be aware of factors that are associated with low or high adherence. This knowledge should reinforce the supporting factors and try to resolve the barriers together with the patient.
To identify factors associated with low or high antiretroviral (ARV) adherence through computational text analysis of an adherence enhancing programme interview reports.
METHODS
Using text from 8428 interviews with 522 patients, we constructed a term-frequency matrix for each patient, retaining words that occurred at least ten times overall and used in at least six interviews with six different patients. The text included both the pharmacist's and the patient's verbalizations. We investigated their association with an adherence threshold (above or below 90%) using a regularized logistic regression model. In addition to this data-driven approach, we studied the contexts of words with a focus group.
RESULTS
Analysis resulted in 7608 terms associated with low or high adherence. Terms associated with low adherence included disruption in daily schedule, side effects, socio-economic factors, stigma, cognitive factors and smoking. Terms associated with high adherence included fixed medication intake timing, no side effects and positive psychological state.
CONCLUSION
Computational text analysis helps to analyze a large corpus of adherence enhancing interviews. It confirms main known themes affecting ARV adherence and sheds light on new emerging themes.
PRACTICE IMPLICATIONS
Health care providers should be aware of factors that are associated with low or high adherence. This knowledge should reinforce the supporting factors and try to resolve the barriers together with the patient.
- Language
-
- English
- Open access status
- closed
- Identifiers
-
- DOI 10.1016/j.pec.2018.05.013
- PMID 29871817
- Persistent URL
- https://sonar.ch/global/documents/46605
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