Common Barriers to Implementation Across Contexts: Evidence to inform the selection of implementation strategies
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,197 | 0,488 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,010 |
| Bibliométrie | 0,013 | 0,011 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,010 | 0,013 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».