Bibliometric analysis of global publications in medication adherence (1900–2017)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Medication non-adherence is a worldwide problem. The aim of this study was to assess the global research output, research trends and topics that shaped medication adherence research. METHODS: A bibliometric methodology was applied. Keywords related to 'medication adherence' were searched in Scopus database for all times up to 31 December 2017. Retrieved data were analyzsd, and bibliometric indicators and maps were presented. KEY FINDINGS: In total, 16 133 documents were retrieved. Most frequently encountered author keywords, other than adherence/compliance, were HIV, hypertension, diabetes mellitus, schizophrenia, depression, osteoporosis, asthma and quality of life. The number of documents published from 2008 to 2017 represented 62.0% (n = 10 005) of the total retrieved documents. The h-index of the retrieved documents was 223. The USA ranked first (43.1%; n = 6959), followed by the UK (8.6%; n = 1384) and Canada (4.5%; n = 796). The USA dominated the lists of active authors and institutions. Top active journals in publishing research on medication adherence were mainly in the field of AIDS. Top-cited articles in the field focused on adherence to anti-HIV medications, the impact of depression on medication adherence and barriers to adherence. CONCLUSION: Adherence among HIV patients dominated the field of medication adherence. Research on medication adherence needs to be strengthened in all countries and in different types of chronic diseases. Research collaboration should also be encouraged to increase research activity on medication adherence in developing countries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.052 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it