Relevance of Electronic Health Information to Doctors in the Developing World: Results of the Ptolemy Project’s Internet‐based Health Information Study (IBHIS)
Bibliographic record
Abstract
The aim of this study was to determine the current usage, relevance, and preferences for electronic health information (EHI) in the participant surgeons' clinical, research, and teaching activities. The Internet-Based Health Information Survey (IBHIS) was conducted from August to December 2003. Thirty-seven doctors (primarily practicing in East Africa) participated, all of whom had been using the Ptolemy resources for at least 6 months. Survey questions concerned time spent reading medical literature, preferred information sources, preferred type of publication, relevance, preference for western versus local medical literature, and academic productivity. Among the 75 eligible participants, 37 (48%) responded. From these responses it was found that African surgeons with access to EHI read more than articles than they did before they had such access, and they find that the information obtained is highly relevant to their clinical, teaching, and research activities. They prefer electronic journals to textbooks and are more inclined to change their practice based on information found in western journals than local journals. Ptolemy resources helped the respondents who reported academic work write a total of 33 papers for presentation or publication. Overall, access to EHI enables doctors in Africa to read more, is relevant, and contributes directly to academic productivity; thus Western medical literature is useful in the developing world, and EHI delivery should continue to expand.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".