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Record W4414585184 · doi:10.1101/2025.09.24.25336521

Common Barriers to Implementation Across Contexts: Evidence to inform the selection of implementation strategies

2025· preprint· en· W4414585184 on OpenAlexaff
Luke Wolfenden, Magdalena Wilczynska, Sam McCrabb, Christophe Lecathelinais, Lucy Couper, Tanja Kuchenmüller, Davi M Mamblona, Nicole Nathan, Justin Presseau, Natalie Taylor, Rachel Sutherland

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsOttawa Hospital
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsContext (archaeology)Psychological interventionData collectionSample (material)Likert scaleSelection (genetic algorithm)Intervention (counseling)Sample size determination

Abstract

fetched live from OpenAlex

ABSTRACT Background The implementation of evidence-informed interventions is required to strengthen health systems and improve health outcomes. Identifying implementation barriers underpins evidence-based approaches to do so, but this process is considered complex and time consuming in practice. Understanding whether certain barriers are more common across contexts may guide the development of more effective implementation strategies when comprehensive primary data collection to assess barriers is not feasible. Methods We conducted a pooled analysis of barrier data from studies that quantitatively assessed implementation barriers using the Theoretical Domains Framework (TDF) survey. To assess barrier frequency aligned to each TDF domain, we calculated the proportion of studies where domain scores were less than four on a standardised 5-point Likert scale. To describe their ‘strength’ we pooled data across studies and reported mean domains scores (lower domain scores represent stronger perceived barriers). Subgroup analyses using pooled domains scores were undertaken to examine differences by population, intervention and geographic characteristics. Results Data from 42 studies published 2012 to 2024, with a combined sample of 9,809 participants were included in the analysis. In 10 of 14 TDF domains, both mean and median scores were 4 or below, indicating that they were typically perceived as barriers. Four domains had a mean score of 4 or below in >80% of all studies that assessed them - reinforcement’, ‘environmental context and resources’, social influences’ and ‘behavioural regulation’ . TDF domains with the lowest scores (representing the strongest barriers), were ‘ environmental context and resources ’; ‘ behavioural regulation ’ and ‘ social influences. ’ Few differences (four of 42 statistical comparisons) were found between TDF domain scores and population, intervention and geographic factors. Conclusions This study identified a set of barriers that appear to be common, and consistent in their perceived strength across a range of population groups, intervention types and geographic localities. The findings provide a basis for those undertaking efforts to improve implementation of evidence-informed health interventions to anticipate types of barriers they may encounter and so, likely strategies that may be needed to address these. This may be beneficial in resource contexts where primary data collection for more comprehensive barrier assessments may not be feasible. CONTRIBUTIONS TO THE LITERATURE While best practice approaches to the development of effective implementation strategies include assessment of local implementation barriers, many improvement initiatives are undertaken by health organisations or practitioners without the collection of primary data using recommended and valid barrier assessment methods. Systematic reviews suggest similar barriers to implementation of health interventions may exist across a range of contexts. We sought to formally investigate such patterning of barriers, and found evidence of a set of barriers that were both prevalent and salient across contexts. As the tacit knowledge, experience and intuition of health professional are typically the basis of improvement initiatives the study provides some guidance to better help those responsible for improving implementation to anticipate common barriers and devise strategies to address them.

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 imitation

Not 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.

metaresearch head score (Codex)0.197
metaresearch head score (Gemma)0.488
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.197
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1970.488
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.010
Bibliometrics0.0130.011
Science and technology studies0.0020.003
Scholarly communication0.0100.013
Open science0.0050.007
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.224
GPT teacher head0.588
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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