Open Access: A Giant Leap towards Bridging Health inequities/Acces Libre Aux Connaissances : Un Pas De Geant Vers le Comblement Des Inegalites En Matiere De sante/Acceso Libre: Un Paso De Gigante Para Resolver Las Inequidades Sanitarias
Bibliographic record
Abstract
Introduction Health knowledge generated in the world's laboratories is passed down the information chain through publications, through its impact and application, its subsequent translation into appropriate contexts for different user communities, arriving finally with health workers and the general public, as the diagram of the knowledge cycle from the Canadian Institutes of Health Research has shown. (1) Studies have shown that access to published health research by the research communities in developing countries is no longer fit for purpose. (2) As has been well documented, rising costs of subscriptions and permission barriers imposed by publishers have barred access to the extent that local health research and health care have been damaged through lack of information. (3,4) For example, Yamey (5) tells of a physician in southern Africa who could not afford full access to journals but based a decision to alter a perinatal HIV prevention programme on one single abstract. The full text article would have shown that the findings were not relevant to the country's situation. With the advent of the internet there is little justification for continuing to create barriers to access. Richard Smith, as the former editor of the British Medical Journal, said, Most research is publicly funded, and when the internet appeared it made no sense for research funders to allow publishers to profit from restricting access to their research. (6) This is true not only for publicly funded research but for private health charities around the world. As the Open Access Policy of the Wellcome Trust states, We ... support unrestricted access to the published output of research as a fundamental part of its charitable mission and a public benefit to be encouraged wherever possible. (7) Science is a collaborative process and openness is fundamental to knowledge advancement. Nowhere has this been shown more clearly than by the 2003 outbreak of SARS (severe acute respiratory syndrome) during which, at the height of the epidemic, there was unprecedented openness and willingness to share critical research information, leading to the identification and the genetic mapping of the responsible coronavirus by 13 collaborating laboratories from 10 countries. (8) The recent release of essential H1N1 data published in several toll-access journals relevant to the H1N1 influenza pandemic points to the recognition that access to health research information is critical in the containment of infectious outbreaks. (9) It is difficult to see how the United Nations' Millennium Development Goals can be achieved without free international access to the world's publicly funded research findings or without collaborative initiatives. Goals 4 to 7 depend on the sharing of research findings for success, while Goal 8, which emphasizes the need for global partnerships for development, recognizes that sharing knowledge and capacity building establish the infrastructure for building future aid programmes. Any solution to the inequality of access to health-care information must be based on the development of an independent and sustainable national research base. Lessons in development aid from the past few decades clearly show that mechanisms that reinforce the dependency culture are no longer appropriate. (10,11) Solutions The United Nation's HINARI, AGORA and OARE programmes, whereby registered libraries or qualified institutions in countries with a Gross Domestic Product (GDP) of
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 | 0.054 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.037 | 0.058 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.093 | 0.019 |
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".