Notice bibliographique
Résumé
Objectives: (a) to show that investment in research is an extremely wise move for any country, (b) to illustrate how research influences health decisions, and (c) to illustrate pathways in the “decolonization” of research in low- and middle-income countries (LMIC). As shown by data compiled by UNESCO (Figure), the Gross Expenditure on Research and Development (GERD) as percent of national GDP greatly enriches its citizens (it’s an investment). For example, the Netherlands (NL), a country with few natural resources, invests heavily in research and has achieved a high level of prosperity for its citizens. Economists concluded that research is an essential investment for a country’s prosperity! The decision-making process by any government is a complex process influenced by budgetary, political, legislated, and other considerations. The importance of “embeddedness” in this process was identified by Koon et-al. who showed that ministerial decisions on health are primarily influenced by people who have a reputation for research and work within the local system. This phenomenon is seen in both rich and poor countries; people who know and understand the culture and context of a country are key influencers on the decision process. The challenges of LMIC to develop capacity in research was the topic of a UNESCO conference in Bamako Mali in 2008. Ministers-of-health from LMICs realized that research is critical in any decision process to ensure “evidence-informed” policy decisions. Their “Call to Action” recognized an essential need to build community-based research capacity to ensure health agendas would be decided by national needs informed by their own researchers. The pathway to decolonization began anew in 1976 with the introduction of Microfinance offering startup funding, capacity-development, and coaching for local people with entrepreneurial ambition. Like Microfinance, MicroResearch (MR) is an innovative, capacity building, community-focused program, empowering local health researchers to improve health in their communities. Conceived in 2008 MR puts local research teams firmly in the driver’s seat from question selection, proposal development to knowledge translation. As of 2022: 19 partner institutions in 8 countries Over 40 two-week training workshops 1,250 health workers and professionals trained Including >300 doctors, >200 nurses and 600 other professions 126 MR teams have launched research projects (over 50 completed) Over 50 PubMed publications and/or policy changes Thousands of lives saved as a result of MR research-initiated changes MR training programs have been incorporated into local institutes MR has shown that high-quality research can be achieved locally when supported by training, coaching and access to small grants. MR decolonizes and democratizes research in LMIC.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,559 | 0,283 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».