Bibliographic record
Abstract
This issue of the Bulletin contains a series of articles on the creation of health information systems in develop-ing countries. A broad range of themes is raised — from what information a properly formed health information sys-tem ought to collect, to rationalizing the collection of information across agencies or institutions, and a consideration of the costs, benefits and potential uses of information that is amassed.The issue of ensuring access to data in developing countries is striking in both similarity to and differences from the situation in developed coun-tries. The main difference is that in developing countries access is limited by a sheer lack of data: concerns and priorities of health information systems are often about collecting information that does not yet exist. Efforts focus on putting basic vital event registries in place, or in making better use of those registries by permitting relatively straightforward linkages of census and death records so that basic demographic measures such as life expectancy can be produced (see AbouZahr & Boerma (pp. 578–583) and Bambas Nolen et al. (597–603)).Similarities lie in the fragmented approaches that are often undertaken to develop new data collections as well as in the lack of explicit attention at the outset to processes that would enable resourceful use of data. The primary rea-son for collection might be programme evaluation or the production of statistics that can be compared across countries, for example. Attention to future data access and use issues prior to data col-lection may help create the capacity to conduct comparisons across countries, to monitor changes over time, and to use the data in combination with other data holdings.In developed countries, such as Canada, the lack of explicit consider-ation of future uses of data in the plan-ning stages of collection has resulted in a fragmented world of data where some
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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 | 0.031 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.021 | 0.037 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 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".