Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".