How is Funding Medical Research Better for Patients?
Notice bibliographique
Résumé
With rising health care costs, often health research is viewed as a major cost driver, calling to question the role and value of provincial funding of health research. Most agree that the quality of healthcare provided is directly linked to our ability to conduct quality research; however currently there is little empirical evidence supporting the link between engagement in health research and healthcare performance. In Canada this has resulted in funding for health research that varies over time and between provinces. While medical knowledge is a public good, we hypothesize there are local benefits from health research, such as the attraction of a specialized human capital workforce, which fosters a culture of innovation in clinical practice. To address this question, we look at whether health outcomes are impacted by changes in provincial research funding in Alberta compared to other provinces. Provincial funding for medical research, which varies greatly over time and among provinces, is used as a proxy for medical treatment inputs. Trend rates of reduction in mortality from potentially avoidable causes (MPAC) (comprised of mortality from preventable causes (MPC) and mortality from treatable causes (MTC)), are used as a proxy health outcome measure sensitive to the contributions of technological progress in medical treatment. Our analysis suggests that investment in health research has payback in health outcomes, with greater improvements in the province where the research occurs. The trend declines seen in age standardized MPAC rates in different Canadian provinces may be impacted by shifts in provincial research funding investment, suggesting that knowledge is not transferred without cost between provinces. Up until the mid-1980s, Alberta had the most rapid rate of decline in MPAC compared to the other provinces. This is striking given the large and unique investment in medical research funding in Alberta in the early 1980s through AHFMR, the only provincial health research funding agency at the time. However in recent years, Alberta’s rate of decrease in MPAC has occurred at a rate slower than the other provinces (British Columbia, Ontario or Quebec) with provincial medical research funding. This is striking at a population level, where Alberta’s failure to achieve a reduction in age standardized rates of MTC comparable to British Columbia, Ontario or Quebec after 1985 represents 240 unnecessary deaths in 2011 and 48,250 Potential Life Years Lost worth around $4.8 billion. The findings from our study suggest that some of the divergence in the rates of reduction in MPAC between provinces may be due to beneficial changes in institutional structure and human capital, resulting in differences across provinces in the capacity to adopt new effective healthcare innovations. While health indicators such as MPAC are the result of complex interactions between the patient, treatment and the healthcare system, as well as socioeconomic and demographic factors, this analysis suggests that a different capacity for health research within the provinces impacts health outcomes. The findings from this analysis are limited by the lack of data related to research funding and the health research workforces within provinces. This analysis has important implications for health research policy and funding allocations, suggesting that decision makers should consider the long-term impact provincial funding for health research has on health outcomes. This study also highlights the lack of longitudinal public data available for provincial health research funding. This information is critical to inform future health research policy.
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 | 0,046 | 0,164 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,008 |
| Études des sciences et des technologies | 0,006 | 0,015 |
| Communication savante | 0,022 | 0,012 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,002 |
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 ».