Low-income Countries' Orthopaedic Information Needs: Challenges and Opportunities
Bibliographic record
Abstract
BACKGROUND: The Internet should, in theory, facilitate access to peer-reviewed scientific articles for orthopaedic surgeons in low-income countries (LIC). However, there are major barriers to access, and most full-text journal articles are available only on a subscription basis, which many in LIC cannot afford. Various models exist to remove such barriers. We set out to examine the potential, and reality, of journal article access for surgeons in LIC by studying readership patterns and journal access through a number of Internet-based initiatives, including an open access journal ("PLoS Medicine"), and programs from the University of Toronto (The Ptolemy Project) and World Health Organization (WHO) (Health InterNetwork Access to Research Initiative [HINARI]). QUESTIONS/PURPOSES: Do Internet-based initiatives that focus on peer-reviewed journal articles deliver clinically relevant information to those who need it? More specifically: (1) Can the WHO's program meet the information needs of practicing surgeons in Africa? (2) Are healthcare workers across the globe aware of, and using, open access journals in a manner that reflects global burden of disease (GBD)? METHODS: We compared actual Ptolemy use to HINARI holdings. We also compared "PLoS Medicine" readership patterns among low-, middle-, and high-income regions. RESULTS: Many of the electronic resources used through Ptolemy are not available through HINARI. In contrast to higher-income regions, "PLoS Medicine" readership in Africa is proportional to both the density of healthcare workers and the GBD there. CONCLUSIONS: Free or low-cost Internet-based initiatives can improve access to the medical literature in LIC. Open access journals are a key component to providing clinically relevant literature to the regions and healthcare workers who need it most.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".