Ethical issues surrounding studies with vulnerable populations: A case study of South African street children
Bibliographic record
Abstract
UNLABELLED: Researchers who investigate social and economic determinants of health often interact with vulnerable and marginalized populations. Great care must be taken to conduct research studies involving vulnerable persons in a manner consistent with accepted ethical principles in order to protect participants from exploitation, to build capacity, and to promote wellbeing. Children form a particularly vulnerable group, especially those who do not enjoy the protection of parents or guardians. METHODS: A research project which studied South African Sunnyside's street children was used as a case study to illustrate ethical issues surrounding research with vulnerable populations. DISCUSSION: The participants in the case study lacked the age of majority and were without any legal guardian. The researchers experienced considerable difficulty in obtaining ethical approval to conduct the study. The street children, at first, were not allowed to give informed consent for the study because of their minor age. Ethical principles of autonomy, disclosure, competence and understanding, consent and voluntariness, beneficence and non-maleficence, and justice are described and applied to this case study involving street children in a South African neighbourhood. It is suggested that by working within an ethical framework, the safety of research participants will be assured and the quality of the research will be enhanced.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".