Privacy Protection for Mobile Health (MHEALTH) in Nigeria: A Consideration of the EU Regime for Data Protection as a Conceptual Model for Reforming Nigeria's Privacy Legislation
Bibliographic record
Abstract
The use of mobile technologies to provide and deliver healthcare is known as Mobile Health. Nigeria is one of the countries witnessing a profound use of these technologies. While discussions have focused on the potentials of this technologies to address the challenges in the health system, nothing is said about the risks from unauthorized disclosure or misuse of health information provided by users. This becomes worse when Nigeria's laws do not offer adequate protection. As Mobile Health is a novelty to Nigeria, this thesis looks to relevant international standards on privacy protection. It does this by examining the European regime for protection of personal information. To prescribe this regime for Nigeria however, the differences in the socio-economic and cultural realities between Nigeria and Europe are presented and examined. This thesis argues that notwithstanding, Nigeria can draw on the European regime to reform its privacy framework.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".