Structurally embedding patient involvement in the health research system: an ongoing quest
Bibliographic record
Abstract
The health research system faces critique for insufficiently matching patients’ needs. To combat this, patient involvement in health research decision-making has been advocated for. Such involvement requires a new way of thinking (culture), organising (structure) and working (practice) for all systems actors. Such a change is hard to achieve: despite patients being involved in various research phases, it is not standard practice and many difficulties remain. This thesis explores the state of the transition towards a system in which involvement is embedded in culture, structure and practice; investigates which factors influence this transition; and experiments with tools and strategies to support transition. I conducted three action-oriented projects. Case one explored child participation in guideline development. We found difficulties around involvement of adults in guideline development appear heightened for children: it hardly happens and children are not taken seriously, child participation is deemed difficult, the structure is rigid and a systems transformation seems not considered urgent by dominant actors. Case one appears in the predevelopment stage, as limited niche experiments by frontrunners have not yet impacted the dominant system. In case two we co-designed a tool stimulating and supporting health researchers to meaningfully involve patients in research design and conduct. Frontrunners and early adaptors are increasingly involving patients. However, involvement is not standard practice, and when it is done it is not always meaningful; it can easily become a checkbox action and impact on decision-making is limited. The traditional, positivistic research culture and rigid structures remain largely unaltered. Prerequisites from funders have not been accompanied by sufficiently accommodating structural changes. This case appears in the take-off phase; some changes to the dominant system appear, but the status quo remains unaltered. In case three, we experimented with the development of a uniquely broad innovation agenda. Patients perspectives not only were important in grounding our broad innovation agenda, this broad approach lead to insights into the issues that hinder the impact of research (agendas). This lead me to critically assess current involvement in agenda-setting. Literature indicates a systems transition for patient involvement in agenda-setting may be reaching the stabilisation phase, as a new culture, structure and practice start to cement. Involvement in agenda-setting has become common practice, and many systems actors have embraced it. Various standardised methods exist, with seemingly little radical innovation. Simultaneously, studies indicate that the impact of such research-agendas on research practice is limited. Thus, stabilisation of the current culture-structure-practice might be undesirable. Despite promising developments, involvement remains an add-on to the traditional system. Interestingly, collective discussion about the functionality of the current health research system, with regards to patient involvement, appears limited. A shared vision of a system in which meaningful involvement is embedded, or the changes that are needed is absent. Consequently, there is little sense of urgency for systems change. Patient involvement risks becoming an ‘intervention’ that must be ‘implemented’, instead of entailing a fundamentally new way of thinking, working and organising. Therefore, transition arenas are required, where different actors collectively discuss the fundamental beliefs, rationales and aims underpinning the health research system. Consequently the role of patient involvement herein and the structural changes required to make this meaningful, should be scrutinized. Experimental new practices and structures should complement this arena. Additionally, case one and two showed that professionals need support to acquire the new competencies required for meaningful involvement. We designed two support tools that stimulate an open and reflexive attitude and stimulate action. In the predevelopment stage, such tools can support frontrunners in experimentation. In later transition stages, they can support a wider group of professionals. However, tools must be accompanied by additional support structures.
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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 | 0.310 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.061 |
| Scholarly communication | 0.054 | 0.049 |
| Open science | 0.009 | 0.051 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".