Bibliometric analysis of global publications in medication adherence (1900–2017)
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.187 | 0.231 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".