Notice bibliographique
Résumé
Recently, the Western world has rediscovered nutrition’s potential for preventing and treating diseases. This led to the emergence of innovative product categories, such as functional foods. Probiotics are an example within this new product category and are defined as “live microorganisms, which when administered in adequate amounts, confer a health benefit on the host”. Numerous studies show probiotics’ potential to address unmet (medical) needs. However, several interrelated barriers have been shown to hinder their effective use by the (medical) community, suggesting probiotic innovation is impeded by systemic barriers. This thesis aims to understand these systemic barriers and their relationship with adoption of probiotics by primary healthcare professionals (HCPs) by answer the research question: “How do the structure and culture element of the primary healthcare system influence the adoption of probiotic interventions by HCPs?” Over 50% of HCPs include probiotics in their recommendations for a variety of indications. However, uncertainty among HCPs regarding their decision to (not) recommend probiotics suggests that advising rates may change over time. Although the effectiveness of probiotics for specific indications partly relies on product choice, up to 43% of HCPs reported experiencing difficulty in selecting a probiotic product. Notably, dieticians were more likely to advise supplements, while GPs more often advised fermented dairy products, which implies that studies looking into probiotics should be clear in their demarcation of the type of probiotic advised, as the implications can be vastly different. Given the substantial heterogeneity in probiotics and IBS patient populations, one may question whether it is feasible but also appropriate to employ a generalization-based method, such as a meta-analysis, to assess efficacy and safety for these type of interventions and indications. Moreover, inconsistent safety reporting seems to complicate comparison of safety data, making it difficult for HCPs to make informed decisions. Notably, HCPs express concerns about the RCTs outcomes’ relevance to their diverse patient populations, as they may not reflect the homogeneity seen in RCTs. Subsequently, HCPs emphasize the importance of research conducted in real-life settings, suggesting user experience research could be valuable to HCPs for evaluating the perceived effectiveness and safety of probiotics. Paradoxically, the absence of substantial evidence from RCTs results in the exclusion of some interventions from clinical guidelines, which HCPs often rely on. Consequently, despite HCPs doubting the usefulness of RCTs, they do play an indirect role in shaping HCPs’ advisory practices. HCPs’ practices are influenced by a combination of perceptions, including the recognition of individual patients’ needs and preferences, reliance on the best available research evidence, and their own clinical expertise. However, there appear to be challenges in reconciling these factors at times, suggesting that balancing these considerations can be complex. We argue that probiotics, once considered a niche innovation, are now gaining widespread acceptance in primary healthcare setting. To foster this transition, we advocate to explore the influence of individual actors in driving change, as well as focusing on incremental changes to give the dominant system and its stakeholders the opportunity to test and refine new approaches.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,009 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,006 |
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 ».