Topic identification, selection, and prioritization for health technology assessment in selected countries: a mixed study design
Notice bibliographique
Résumé
BACKGROUND: There is limited evidence-informed guidance on TISP processes for countries where health technology assessment (HTA) is in a nascent phase. We aimed to explore the range of topic identification, selection and prioritization (TISP) processes and practices for HTA in selected countries and identify aspects relevant to emerging HTA systems. METHODS: This mixed design study included a systematic literature review, an electronic survey, and individual interviews. We conducted a systematic literature review with criteria that were developed a priori to identify countries deemed to have a recently formalized HTA system. Based on the literature review, a twenty-three item online survey was shared with the identified countries, we completed follow-up interviews with ten participants who have experience with HTA. We analyzed documents, survey responses and interview transcripts thematically to identify lessons related to TISP processes and practices. RESULTS: The literature review identified 29 nine candidate countries as having a "potential" recently formalized HTA system. Twenty-one survey responses were analyzed and supplemented with ten individual interviews. We found variation in countries' approaches to TISP - particularly between pharmaceutical and non-pharmaceutical interventions. Results indicate that TISP is heavily driven by policy makers, expert involvement, and to a lesser extent, relevant stakeholders. The use of horizon-scanning and early warning systems is uncommon. Interviewee participants provided further insight to the survey data, reporting that political awareness and an institutional framework were important to support TISP. TISP can be optimized by stronger national regulations and legislative structures, in addition to education and advocacy about HTA among politicians and decision-makers. In some settings regional networks have been useful, particularly in the development of TISP guidelines and methodologies. Additionally, the technical capacity to conduct TISP, and access to relevant local data were factors limiting TISP in national settings. Increased network collaboration and capacity building were reported as future needs. CONCLUSIONS: This study provides current insights into a topic where there is limited published peer reviewed literature. TISP is an important first step of HTA, and topics should be selected and prioritized based on local need and relevance. The limited capacity for TISP in settings where HTA is emerging may be supported by local and international collaboration to increase capacity and knowledge. To succeed, both TISP and HTA need to be embedded within national health care priority setting and decision-making. More in-depth understanding of where countries are situtated in formalizing the TISP process may help others to overcome factors that facilitate or hinder progress.
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 | 0,155 | 0,151 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,008 | 0,008 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».