Notice bibliographique
Résumé
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.
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 | 0,165 | 0,248 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,007 | 0,006 |
| Études des sciences et des technologies | 0,011 | 0,139 |
| Communication savante | 0,031 | 0,057 |
| Science ouverte | 0,010 | 0,026 |
| Intégrité de la recherche | 0,018 | 0,026 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,004 |
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 ».