Bibliographic record
Abstract
Contemporary healthcare governance involves determining whether and under what conditions access to interventions on the healthcare market can be granted. The accountability of these sensitive policy choices is grounded in the scientific knowledge practices they are informed by. Technology assessment has become an important mechanism of governance where scientific analysis advises policy-makers on the societal consequences associated with the uptake and application of technology. In the healthcare sector, health technology assessment (HTA) often takes the form of an expert-led multidisciplinary exercise that uses explicit methodologies to determine the added value of health technologies to national healthcare systems. The gradual institutionalisation of HTA – particularly in Europe – has led to recent legislation that promotes greater harmonisation and cross-national cooperation in assessment practice. As these efforts aim to consolidate scientific quality in HTA, they bring other questions to the surface. In a context where recognition of the diversity of knowledge has become increasingly prominent within institutional discourse, prior critiques of assessment practice have often landed on its privileging of specialist, disciplinary expertise in the determination of health technology’s value. Discussions in the field in parallel have focused on how HTA analysis can better address the complexities of health technologies in practice, through the inclusion of patients’ and technology users’ knowledge. This thesis is concerned with the challenge of how institutional assessments of health technologies can make room for different and incommensurate forms of knowledge, or, how to foster reflexivity within the science of HTA. These are not particularly new questions, yet they have persisted within both HTA and science and technology studies (STS) scholarship studying HTA. Taking an experimental approach to the inclusion of patient knowledge in assessment practices, this thesis attempts to connect these two fields through new forms of intervention. The empirical content is drawn predominantly from a 2.5-year project within the European Union’s Innovative Medicines Initiative (IMI) - a public-private partnership that funds various collaborations between health technology development stakeholders. The consortium focused on developing and implementing resources for strengthening patient engagement in institutional health technology evaluation. Participating in and facilitating this collaborative work in IMI-PARADIGM formed the basis of the transdisciplinary experimentation and analysis presented in this thesis. Part I, figuring the problem space, explores the institutionalising practice of including and incorporating patients’ perspectives within evaluations of health technology and the consequences of such for a reflexive HTA. Chapter 4 shows how different parameters of assessment become reframed upon the positioning of patient knowledge alongside other forms of evidence and expertise. Chapter 5 shows that whilst patient engagement within evidence generation activities for HTA may mobilise forms of reflexive learning, the chapter highlights sedimented institutional boundaries and dominant knowledge that impede reflexive forms of assessment. Part II, transdisciplinary experimentation, deals with how the empirical and experimental study of scientific knowledge practices can contribute to their co-shaping. Analysing the politics of the IMI-PARADIGM partnership in more detail, Chapters 6 and 7 explore transdisciplinary experimentation as a means of stimulating reflexive assessment from within institutional practices. Finally, Chapter 8 asks what becomes of scholarly practices that intervene through transdisciplinary experimentation. The thesis shows that patients’ involvement in the science of HTA presents opportunities for institutionalised forms of reflexivity, but, concurrently, requires careful ways of fostering engagement between the different forms of knowledge and expertise needed to (re-)figure the problem space of health technology assessment. Such a challenge invites experimentation with dominant knowledge practices in order to foster change from within. Learning from experimentation, in matching up knowledges that don’t necessarily match, will be part of the steps required from here to take seriously the challenge of reflexive assessment.
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 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 | 0.165 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.139 |
| Scholarly communication | 0.031 | 0.057 |
| Open science | 0.010 | 0.026 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".