DOI: 10.5281/zenodo.18108976
title: Health-related "Barriers and Facilitators" Pubmed meta-data from January 1, 1980 to December 30, 2025

description: This dataset contains a large-scale corpus of PubMed abstracts and associated metadata assembled to examine the evolution, prevalence, and analytic consequences of barriers-and-facilitators framing in implementation-related public and community health research. The dataset was created to support empirical analyses of how implementation challenges are described at scale, with particular attention to prioritization, conceptual concentration, and the representation of structural determinants.

Data source and scope

Records were retrieved from PubMed using the NCBI E-utilities API between December 23 and December 30, 2025. The corpus includes English-language abstracts published between January 1, 1980 and the most recent indexing available at the time of retrieval (including early-indexed 2026 records). Duplicate records were removed using PubMed identifiers (PMIDs).

The final dataset comprises approximately 1.5 million unique abstracts, spanning multiple decades of research in public health, community health, implementation science, and related practice-oriented domains.

Search strategy

The corpus was assembled using a deliberately high-sensitivity, two-part Boolean search strategy:





Barriers and facilitators clause, including wildcarded and phrase-based terms such as barrier*, facilitat*, challenge*, constraint*, bottleneck*, and exact phrases such as “barriers and facilitators,” “implementation barriers,” and “perceived facilitators.”




Context clause, capturing implementation-relevant public and community health settings and processes, including public health, community health, health equity, implementation science, dissemination, adoption, scale-up, quality improvement, primary care, and community-based practice.



All terms were searched across [All Fields] to maximize recall and reduce bias introduced by indexing variability.

Contents and variables

Each record in the dataset corresponds to a single PubMed abstract and includes bibliographic, textual, and derived analytic variables. Core fields include:





Identifiers: PMID, DOI (where available), PMC ID




Publication metadata: journal name and abbreviation, publication year, month, and day




Textual content: article title and abstract




Indexing metadata: MeSH terms and MeSH codes (when available)




Authorship and affiliations: author lists and institutional affiliations (as provided by PubMed)




Funding and disclosures: grant identifiers and conflict-of-interest fields (when available)




Search provenance: query identifiers and year-based cohort variables used for analysis



In addition to raw metadata, the dataset includes derived classification variables used in the accompanying analyses, such as indicators for implementation relevance, explicit barriers-and-facilitators framing, ambient barrier language, and context-only implementation discourse.

Intended use

The dataset is intended for researchers interested in:





Meta-research on implementation science and public health practice




Large-scale text analysis of scientific discourse




Examination of how barriers, facilitators, and implementation challenges are framed over time




Studies of prioritization, decision-relevance, and conceptual saturation in applied research literatures



While the dataset was developed to support a specific empirical investigation, it is suitable for secondary analyses, replication studies, and methodological research on scientific language and framing.

Limitations

The dataset is based on abstracts rather than full-text articles and therefore reflects how studies are summarized rather than all analytic details contained in the full publications. Inclusion in the corpus reflects the search strategy and should not be interpreted as exhaustive coverage of all implementation research. Derived classification variables are based on keyword and pattern matching and are intended for population-level analysis rather than article-level evaluation.